Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
dense
Generated from Trainer
dataset_size:138875
loss:MatryoshkaLoss
loss:CachedGISTEmbedLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use epaulson2/medical-gte-base-eldercare with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use epaulson2/medical-gte-base-eldercare with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("epaulson2/medical-gte-base-eldercare") sentences = [ "Represent this sentence for searching relevant passages: What is HERS Miconazole 3 used for?", "Indications For the temporary relief of skin irritations Directions Adults: Take five granules three times daily or as recommended by your healthcare practitioner. Children: Take three granules and follow adult directions.", "Warnings Do not use on children under 2 years of age unless directed by a doctor. For external use only avoid contact with eyes. Irritation occurs or if there is no improvement within 4 weeks (for athlete's foot and ringworm)irritation occurs or if there is no improvement within 2 weeks (for jock itch).", "Uses • treats vaginal yeast infections" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "word_embedding_dimension": 768, | |
| "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 | |
| } |