Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:132037
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use Phora68/rapha-embed-clinical-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Phora68/rapha-embed-clinical-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Phora68/rapha-embed-clinical-v1") sentences = [ "back pain. I'm not sure what to make of it.", "Observed: back pain — musculoskeletal system", "Patient is frustrated with the medical system", "OPQRST — Severity: fever rated 7/10 by patient" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "__version__": { | |
| "pytorch": "2.11.0+cu128", | |
| "sentence_transformers": "5.6.0", | |
| "transformers": "5.12.1" | |
| }, | |
| "default_prompt_name": null, | |
| "model_type": "SentenceTransformer", | |
| "prompts": { | |
| "document": "", | |
| "query": "" | |
| }, | |
| "similarity_fn_name": "cosine" | |
| } |