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E-MIMIC
/
inclusively-classification

Text Classification
Transformers
PyTorch
Safetensors
bert
Model card Files Files and versions
xet
Community
1

Instructions to use E-MIMIC/inclusively-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use E-MIMIC/inclusively-classification with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="E-MIMIC/inclusively-classification")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("E-MIMIC/inclusively-classification")
    model = AutoModelForSequenceClassification.from_pretrained("E-MIMIC/inclusively-classification", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
inclusively-classification
886 MB
Ctrl+K
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  • 4 contributors
History: 6 commits
grecosalvatore's picture
grecosalvatore
Update README.md
6875dbf verified about 1 year ago
  • .gitattributes
    1.52 kB
    initial commit about 3 years ago
  • README.md
    3.03 kB
    Update README.md about 1 year ago
  • config.json
    890 Bytes
    Adding model and first version of the README about 3 years ago
  • model.safetensors
    443 MB
    xet
    Adding `safetensors` variant of this model (#1) over 1 year ago
  • pytorch_model.bin
    443 MB
    xet
    Adding model and first version of the README about 3 years ago
  • tokenizer_config.json
    59 Bytes
    Adding model and first version of the README about 3 years ago
  • training_args.bin
    2.99 kB
    xet
    Adding model and first version of the README about 3 years ago
  • vocab.txt
    235 kB
    Adding model and first version of the README about 3 years ago