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n2vec
/
cross-encoder_ms-marco-MiniLM-L-6-v2

Text Classification
Transformers
PyTorch
JAX
bert
Model card Files Files and versions
xet
Community
2

Instructions to use n2vec/cross-encoder_ms-marco-MiniLM-L-6-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use n2vec/cross-encoder_ms-marco-MiniLM-L-6-v2 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="n2vec/cross-encoder_ms-marco-MiniLM-L-6-v2")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("n2vec/cross-encoder_ms-marco-MiniLM-L-6-v2")
    model = AutoModelForSequenceClassification.from_pretrained("n2vec/cross-encoder_ms-marco-MiniLM-L-6-v2")
  • Notebooks
  • Google Colab
  • Kaggle
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Update model metadata to set pipeline tag to the new `text-ranking` and library name to `sentence-transformers`

#2 opened about 1 year ago by
tomaarsen

Adding `safetensors` variant of this model

#1 opened about 1 year ago by
SFconvertbot
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