Sentence Similarity
sentence-transformers
ONNX
Safetensors
OpenVINO
modernbert
loss:OnlineContrastiveLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use redis/langcache-embed-medical-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use redis/langcache-embed-medical-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("redis/langcache-embed-medical-v1") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Add model files
#2
by srijithrajamohan - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:1f13cd98a2b1fa07f1aba715936cd199e883d25a5c3a57be5d157d57977cdfb7
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size 596070136
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