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LDCC
/
bge-reranker-v2-m3

Text Classification
Transformers
ONNX
xlm-roberta
text-embeddings-inference
Model card Files Files and versions
xet
Community
2

Instructions to use LDCC/bge-reranker-v2-m3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use LDCC/bge-reranker-v2-m3 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="LDCC/bge-reranker-v2-m3")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("LDCC/bge-reranker-v2-m3")
    model = AutoModelForSequenceClassification.from_pretrained("LDCC/bge-reranker-v2-m3")
  • Notebooks
  • Google Colab
  • Kaggle
bge-reranker-v2-m3
2.29 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 9 commits
psyche's picture
psyche
Upload XLMRobertaForSequenceClassification
18dc2e2 over 1 year ago
  • .gitattributes
    1.62 kB
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  • README.md
    5.17 kB
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  • config.json
    808 Bytes
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  • model.onnx
    695 kB
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  • model.onnx_data
    2.27 GB
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  • sentencepiece.bpe.model
    5.07 MB
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  • special_tokens_map.json
    964 Bytes
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  • tokenizer.json
    17.1 MB
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  • tokenizer_config.json
    1.17 kB
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