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 new SentenceTransformer model with an onnx backend
#9
by waris-gill - opened
- onnx/model.onnx +3 -0
onnx/model.onnx
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:a93600476392fcc9c7b2e7a70e02562579b5592116c482223b0555a6cefa08ee
|
| 3 |
+
size 596472567
|