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xpmir
/
cross-encoder-MiniLM-L12-DistillRankNET

Text Classification
Transformers
TensorBoard
Safetensors
English
bert
cross-encoder
sequence-classification
text-embeddings-inference
Model card Files Files and versions
xet
Metrics Training metrics Community

Instructions to use xpmir/cross-encoder-MiniLM-L12-DistillRankNET with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use xpmir/cross-encoder-MiniLM-L12-DistillRankNET with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="xpmir/cross-encoder-MiniLM-L12-DistillRankNET")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("xpmir/cross-encoder-MiniLM-L12-DistillRankNET")
    model = AutoModelForSequenceClassification.from_pretrained("xpmir/cross-encoder-MiniLM-L12-DistillRankNET")
  • Notebooks
  • Google Colab
  • Kaggle
cross-encoder-MiniLM-L12-DistillRankNET
150 MB
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  • 1 contributor
History: 7 commits
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VictorMorand
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  • model.safetensors
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  • tokenizer.json
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  • tokenizer_config.json
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