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
- Xet hash:
- bc9934591a3b3d853739a4398c95d57101074e22af15875ff81b70581f190785
- Size of remote file:
- 266 MB
- SHA256:
- 4380c3b61228b6eb259dda0daac5b79783b98a61ab197893b3e575e054d3c4c2
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