Sentence Similarity
sentence-transformers
ONNX
Safetensors
OpenVINO
modernbert
loss:OnlineContrastiveLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use redis/langcache-embed-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use redis/langcache-embed-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("redis/langcache-embed-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] - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d451aefa30bd4b2e9dcb1296d35bad66f0d6fa4ff4f6ba42dc46aa4b54e6a4f0
- Size of remote file:
- 596 MB
- SHA256:
- a8463d309f5f9f72f370db7ea48a7eca5a7b16373c94b5162a273032a5b98587
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.