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optimum
/
sbert-all-MiniLM-L6-with-pooler

Sentence Similarity
sentence-transformers
ONNX
English
bert
feature-extraction
text-embeddings-inference
Model card Files Files and versions
xet
Community
1

Instructions to use optimum/sbert-all-MiniLM-L6-with-pooler with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use optimum/sbert-all-MiniLM-L6-with-pooler with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("optimum/sbert-all-MiniLM-L6-with-pooler")
    
    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
sbert-all-MiniLM-L6-with-pooler
91.9 MB
Ctrl+K
Ctrl+K
  • 2 contributors
History: 4 commits
Jingya's picture
Jingya HF Staff
Update README with custom snippet
68c50b1 almost 4 years ago
  • .gitattributes
    1.34 kB
    initial commit almost 4 years ago
  • README.md
    9.2 kB
    Update README with custom snippet almost 4 years ago
  • config.json
    683 Bytes
    Upload onnx model almost 4 years ago
  • model.onnx
    90.9 MB
    xet
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  • special_tokens_map.json
    125 Bytes
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  • tokenizer.json
    712 kB
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  • tokenizer_config.json
    593 Bytes
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  • vocab.txt
    232 kB
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