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
Formatting fixes
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by srijithrajamohan - opened
README.md
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similarities = model.similarity(embeddings, embeddings)
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print(similarities.shape)
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####
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| Metric | Value |
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## Training Details
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### Training Dataset
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#### csv
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similarities = model.similarity(embeddings, embeddings)
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print(similarities.shape)
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```
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#### Binary Classification
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| Metric | Value |
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| **cosine_ap** | |
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### Training Dataset
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#### csv
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