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macavaney
/
deepct

Token Classification
Transformers
PyTorch
Safetensors
English
bert
retrieval
document-rewriting
Model card Files Files and versions
xet
Community
1

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

  • Libraries
  • Transformers

    How to use macavaney/deepct with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("token-classification", model="macavaney/deepct")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForTokenClassification
    
    tokenizer = AutoTokenizer.from_pretrained("macavaney/deepct")
    model = AutoModelForTokenClassification.from_pretrained("macavaney/deepct")
  • Notebooks
  • Google Colab
  • Kaggle
deepct
872 MB
Ctrl+K
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  • 3 contributors
History: 3 commits
macavaney's picture
macavaney
SFconvertbot's picture
SFconvertbot
Adding `safetensors` variant of this model (#1)
913f6ed almost 3 years ago
  • .gitattributes
    1.48 kB
    initial commit over 3 years ago
  • README.md
    809 Bytes
    initial commit over 3 years ago
  • config.json
    752 Bytes
    initial commit over 3 years ago
  • model.safetensors
    436 MB
    xet
    Adding `safetensors` variant of this model (#1) almost 3 years ago
  • pytorch_model.bin
    436 MB
    xet
    initial commit over 3 years ago
  • special_tokens_map.json
    125 Bytes
    initial commit over 3 years ago
  • tokenizer.json
    711 kB
    initial commit over 3 years ago
  • tokenizer_config.json
    348 Bytes
    initial commit over 3 years ago
  • vocab.txt
    232 kB
    initial commit over 3 years ago