Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:77455
loss:ContrastiveLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use EvgenyBondarenko/BIEncoderRanker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use EvgenyBondarenko/BIEncoderRanker with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("EvgenyBondarenko/BIEncoderRanker") sentences = [ "Исследование антигена хеликобактера (Helicobacter pylori)", "Токсоплазма (Toxoplasma gondii): Антитела: IgG, (количественно). Метод: ИФА", "Хеликобактер пилори (Helicobacter pylori): Антитела: IgG, (количественно). Метод: ИФА", "УЗИ молочных желез с эластографией" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download 1_Pooling/config.json from EvgenyBondarenko/BIEncoderRanker: direct link, hf CLI and curl.
- Browser
- Download file 297 Bytes
-
https://huggingface.co/EvgenyBondarenko/BIEncoderRanker/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://EvgenyBondarenko/BIEncoderRanker/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/EvgenyBondarenko/BIEncoderRanker/resolve/main/1_Pooling/config.json
297 Bytes
| { | |
| "word_embedding_dimension": 1024, | |
| "pooling_mode_cls_token": true, | |
| "pooling_mode_mean_tokens": false, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false, | |
| "pooling_mode_weightedmean_tokens": false, | |
| "pooling_mode_lasttoken": false, | |
| "include_prompt": true | |
| } |