Sentence Similarity
sentence-transformers
PyTorch
Safetensors
English
mpnet
feature-extraction
medical
biology
Instructions to use FremyCompany/BioLORD-2023-C with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use FremyCompany/BioLORD-2023-C with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("FremyCompany/BioLORD-2023-C") sentences = [ "bartonellosis", "cat scratch disease", "cat scratch wound", "tick-borne orbivirus fever", "cat fur" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [5, 5] - Inference
- Notebooks
- Google Colab
- Kaggle
| {"do_lower_case": true, "bos_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "eos_token": {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "sep_token": {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "cls_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "unk_token": {"content": "[UNK]", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "pad_token": {"content": "<pad>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "tokenize_chinese_chars": true, "strip_accents": null, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "output/sick_sts_mednlisim_medsts_defs4b_medcycle1_stamb-2023-09-17_20-30-20/0_Transformer/", "tokenizer_class": "MPNetTokenizer", "do_basic_tokenize": true, "never_split": null} |