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RedHatAI
/
bge-large-en-v1.5-sparse

Feature Extraction
Transformers
ONNX
English
bert
sparse sparsity quantized onnx embeddings int8
mteb
Eval Results (legacy)
Model card Files Files and versions
xet
Community

Instructions to use RedHatAI/bge-large-en-v1.5-sparse with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use RedHatAI/bge-large-en-v1.5-sparse with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="RedHatAI/bge-large-en-v1.5-sparse")
    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("RedHatAI/bge-large-en-v1.5-sparse")
    model = AutoModel.from_pretrained("RedHatAI/bge-large-en-v1.5-sparse")
  • Notebooks
  • Google Colab
  • Kaggle
bge-large-en-v1.5-sparse
432 MB
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  • 1 contributor
History: 20 commits
zeroshot's picture
zeroshot
Update README.md
1429ba1 over 2 years ago
  • .gitattributes
    1.52 kB
    initial commit over 2 years ago
  • README.md
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  • config.json
    742 Bytes
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  • model.onnx
    431 MB
    xet
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  • tokenizer.json
    711 kB
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