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

Feature Extraction
Transformers
ONNX
English
bert
mteb
sparse sparsity quantized onnx embeddings int8
Eval Results (legacy)
Model card Files Files and versions
xet
Community
1

Instructions to use RedHatAI/bge-base-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-base-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-base-en-v1.5-sparse")
    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("RedHatAI/bge-base-en-v1.5-sparse")
    model = AutoModel.from_pretrained("RedHatAI/bge-base-en-v1.5-sparse")
  • Notebooks
  • Google Colab
  • Kaggle
bge-base-en-v1.5-sparse
182 MB
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  • 1 contributor
History: 22 commits
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zeroshot
Update README.md
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  • .gitattributes
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  • README.md
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  • config.json
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  • model.onnx
    181 MB
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  • tokenizer.json
    711 kB
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