Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:9020
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use Devy1/MiniLM-cosqa-64 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Devy1/MiniLM-cosqa-64 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Devy1/MiniLM-cosqa-64") sentences = [ "python multiprocessing show cpu count", "def unique(seq):\n \"\"\"Return the unique elements of a collection even if those elements are\n unhashable and unsortable, like dicts and sets\"\"\"\n cleaned = []\n for each in seq:\n if each not in cleaned:\n cleaned.append(each)\n return cleaned", "def is_in(self, point_x, point_y):\n \"\"\" Test if a point is within this polygonal region \"\"\"\n\n point_array = array(((point_x, point_y),))\n vertices = array(self.points)\n winding = self.inside_rule == \"winding\"\n result = points_in_polygon(point_array, vertices, winding)\n return result[0]", "def machine_info():\n \"\"\"Retrieve core and memory information for the current machine.\n \"\"\"\n import psutil\n BYTES_IN_GIG = 1073741824.0\n free_bytes = psutil.virtual_memory().total\n return [{\"memory\": float(\"%.1f\" % (free_bytes / BYTES_IN_GIG)), \"cores\": multiprocessing.cpu_count(),\n \"name\": socket.gethostname()}]" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "added_tokens_decoder": { | |
| "0": { | |
| "content": "[PAD]", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "100": { | |
| "content": "[UNK]", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "101": { | |
| "content": "[CLS]", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "102": { | |
| "content": "[SEP]", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "103": { | |
| "content": "[MASK]", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| } | |
| }, | |
| "clean_up_tokenization_spaces": false, | |
| "cls_token": "[CLS]", | |
| "do_basic_tokenize": true, | |
| "do_lower_case": true, | |
| "extra_special_tokens": {}, | |
| "mask_token": "[MASK]", | |
| "max_length": 128, | |
| "model_max_length": 256, | |
| "never_split": null, | |
| "pad_to_multiple_of": null, | |
| "pad_token": "[PAD]", | |
| "pad_token_type_id": 0, | |
| "padding_side": "right", | |
| "sep_token": "[SEP]", | |
| "stride": 0, | |
| "strip_accents": null, | |
| "tokenize_chinese_chars": true, | |
| "tokenizer_class": "BertTokenizer", | |
| "truncation_side": "right", | |
| "truncation_strategy": "longest_first", | |
| "unk_token": "[UNK]" | |
| } | |