Sentence Similarity
sentence-transformers
PyTorch
Safetensors
English
mpnet
feature-extraction
medical
biology
text-embeddings-inference
Instructions to use FremyCompany/BioLORD-2023 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use FremyCompany/BioLORD-2023 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("FremyCompany/BioLORD-2023") 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
Download 1_Pooling/config.json from FremyCompany/BioLORD-2023: direct link, hf CLI and curl.
- Browser
- Download file 190 Bytes
-
https://huggingface.co/FremyCompany/BioLORD-2023/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://FremyCompany/BioLORD-2023/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/FremyCompany/BioLORD-2023/resolve/main/1_Pooling/config.json
190 Bytes
| { | |
| "word_embedding_dimension": 768, | |
| "pooling_mode_cls_token": false, | |
| "pooling_mode_mean_tokens": true, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false | |
| } |