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Straiberry
/
Treatment_Recommendation

Text Classification
Transformers
PyTorch
distilbert
text-embeddings-inference
Model card Files Files and versions
xet
Community
1

Instructions to use Straiberry/Treatment_Recommendation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Straiberry/Treatment_Recommendation with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="Straiberry/Treatment_Recommendation")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("Straiberry/Treatment_Recommendation")
    model = AutoModelForSequenceClassification.from_pretrained("Straiberry/Treatment_Recommendation")
  • Notebooks
  • Google Colab
  • Kaggle
Treatment_Recommendation
269 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 2 commits
Pooya's picture
Pooya
end of training
f4aaac9 about 3 years ago
  • .gitattributes
    1.48 kB
    initial commit about 3 years ago
  • config.json
    997 Bytes
    end of training about 3 years ago
  • pytorch_model.bin
    268 MB
    xet
    end of training about 3 years ago
  • special_tokens_map.json
    125 Bytes
    end of training about 3 years ago
  • tokenizer.json
    711 kB
    end of training about 3 years ago
  • tokenizer_config.json
    360 Bytes
    end of training about 3 years ago
  • training_args.bin
    3.64 kB
    xet
    end of training about 3 years ago