Instructions to use tner/roberta-base-tweetner7-2020 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tner/roberta-base-tweetner7-2020 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="tner/roberta-base-tweetner7-2020")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("tner/roberta-base-tweetner7-2020") model = AutoModelForTokenClassification.from_pretrained("tner/roberta-base-tweetner7-2020", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from tner/roberta-base-tweetner7-2020: direct link, hf CLI and curl.
- Browser
- Download file 481 Bytes
-
https://huggingface.co/tner/roberta-base-tweetner7-2020/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://tner/roberta-base-tweetner7-2020/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/tner/roberta-base-tweetner7-2020/resolve/main/tokenizer_config.json
481 Bytes
| {"errors": "replace", "bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "<unk>", "pad_token": "<pad>", "mask_token": "<mask>", "add_prefix_space": false, "trim_offsets": true, "model_max_length": 512, "name_or_path": "cner_output/model/baseline/roberta_base/model_mwzvua/epoch_10", "special_tokens_map_file": "cner_output/model/baseline/roberta_base/model_mwzvua/epoch_10/special_tokens_map.json", "tokenizer_class": "RobertaTokenizer"} |