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metarank
/
all-MiniLM-L6-v2

Feature Extraction
sentence-transformers
ONNX
English
sentence-similarity
Model card Files Files and versions
xet
Community

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

  • Libraries
  • sentence-transformers

    How to use metarank/all-MiniLM-L6-v2 with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("metarank/all-MiniLM-L6-v2")
    
    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
91.7 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 6 commits
shuttie's picture
shuttie
add tokenizer.json
b142875 over 2 years ago
  • .gitattributes
    140 Bytes
    add tokenizer.json over 2 years ago
  • .gitignore
    5 Bytes
    initial commit about 3 years ago
  • README.md
    1.51 kB
    add readme and model card about 3 years ago
  • convert.py
    773 Bytes
    set proper output layer name about 3 years ago
  • pytorch_model.onnx
    91 MB
    xet
    set proper output layer name about 3 years ago
  • requirements.txt
    79 Bytes
    add readme and model card about 3 years ago
  • tokenizer.json
    466 kB
    xet
    add tokenizer.json over 2 years ago
  • vocab.txt
    232 kB
    xet
    add vocab.txt about 3 years ago