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aapot
/
bge-m3-onnx

Feature Extraction
Transformers
ONNX
xlm-roberta
text-embeddings-inference
Model card Files Files and versions
xet
Community
5

Instructions to use aapot/bge-m3-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use aapot/bge-m3-onnx with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="aapot/bge-m3-onnx")
    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("aapot/bge-m3-onnx")
    model = AutoModel.from_pretrained("aapot/bge-m3-onnx", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
bge-m3-onnx
2.29 GB
Ctrl+K
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  • 2 contributors
History: 3 commits
aapot's picture
aapot
Update README.md
76a6033 verified over 2 years ago
  • .gitattributes
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  • Constant_685_attr__value
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  • README.md
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  • bgem3_model.py
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  • config.json
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  • export_onnx.py
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  • model.onnx
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  • model.onnx.data

    Pickle imports

    • No problematic imports detected

    What is a pickle import?

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  • ort_config.json
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  • requirements.txt
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  • sentencepiece.bpe.model
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  • special_tokens_map.json
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  • tokenizer.json
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  • tokenizer_config.json
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