Sentence Similarity
sentence-transformers
Safetensors
English
bert
biencoder
text-classification
sentence-pair-classification
semantic-similarity
semantic-search
retrieval
reranking
Generated from Trainer
dataset_size:8000000
loss:ArcFaceInBatchLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use redis/langcache-embed-v3-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use redis/langcache-embed-v3-small with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("redis/langcache-embed-v3-small") sentences = [ "\"How much would I need to narrate a \"\"Let's Play\"\" video in order to make money from it on YouTube?\"", "How much money do people make from YouTube videos with 1 million views?", "\"How much would I need to narrate a \"\"Let's Play\"\" video in order to make money from it on YouTube?\"", "\"Does the sentence, \"\"I expect to be disappointed,\"\" make sense?\"" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Inference
- Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
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@@ -142,7 +142,7 @@ Then you can load this model and run inference.
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from sentence_transformers import SentenceTransformer
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# Download from the 🤗 Hub
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model = SentenceTransformer("redis/langcache-embed-v3
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# Run inference
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sentences = [
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'"If you click ""like"" on an old post that someone made on your wall yet you\'re no longer Facebook friends, will they still receive a notification?"',
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from sentence_transformers import SentenceTransformer
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# Download from the 🤗 Hub
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model = SentenceTransformer("redis/langcache-embed-v3-small")
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# Run inference
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sentences = [
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'"If you click ""like"" on an old post that someone made on your wall yet you\'re no longer Facebook friends, will they still receive a notification?"',
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