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ncbi
/
MedCPT-Cross-Encoder

Text Classification
Transformers
PyTorch
bert
text-embeddings-inference
Model card Files Files and versions
xet
Community
4

Instructions to use ncbi/MedCPT-Cross-Encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use ncbi/MedCPT-Cross-Encoder with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="ncbi/MedCPT-Cross-Encoder")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("ncbi/MedCPT-Cross-Encoder")
    model = AutoModelForSequenceClassification.from_pretrained("ncbi/MedCPT-Cross-Encoder")
  • Inference
  • Notebooks
  • Google Colab
  • Kaggle
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  • Code of Conduct
  • Hub documentation

Update model metadata to set pipeline tag to the new `text-ranking` and library name to `sentence-transformers`

#4 opened about 1 year ago by
tomaarsen

Adding `safetensors` variant of this model

#3 opened about 1 year ago by
SFconvertbot

Adding `safetensors` variant of this model

#2 opened about 1 year ago by
SFconvertbot

Adding `safetensors` variant of this model

#1 opened over 1 year ago by
SFconvertbot
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