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EmbeddedLLM
/
all-MiniLM-L6-v2-onnx-o3-cpu

Feature Extraction
sentence-transformers
ONNX
English
bert
sentence-similarity
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use EmbeddedLLM/all-MiniLM-L6-v2-onnx-o3-cpu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use EmbeddedLLM/all-MiniLM-L6-v2-onnx-o3-cpu with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("EmbeddedLLM/all-MiniLM-L6-v2-onnx-o3-cpu")
    
    sentences = [
        "The weather is lovely today.",
        "It's so sunny outside!",
        "He drove to the stadium."
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [3, 3]
  • Notebooks
  • Google Colab
  • Kaggle
all-MiniLM-L6-v2-onnx-o3-cpu
91.2 MB
Ctrl+K
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  • 2 contributors
History: 6 commits
thesunday's picture
thesunday
Update README.md
3b9f0aa verified over 2 years ago
  • .gitattributes
    1.52 kB
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  • README.md
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  • config.json
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  • model.onnx
    90.3 MB
    xet
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  • ort_config.json
    1.27 kB
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  • special_tokens_map.json
    695 Bytes
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  • tokenizer.json
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  • tokenizer_config.json
    1.43 kB
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  • vocab.txt
    232 kB
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