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:
- 3edfe04fb8d27598cc48fe9d677f6923f92e835e453d8309e2ca0cbd72f822df
- Size of remote file:
- 266 MB
- SHA256:
- 6b216fa8e3b36e29aad11cafefe7ec1228d7409ffec58c95c2e9d0c826bcc23c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.