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jinhybr
/
OCR-LayoutLMv3

Token Classification
Transformers
PyTorch
TensorBoard
layoutlmv3
Generated from Trainer
Eval Results (legacy)
Model card Files Files and versions
xet
Metrics Training metrics Community
2

Instructions to use jinhybr/OCR-LayoutLMv3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use jinhybr/OCR-LayoutLMv3 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("token-classification", model="jinhybr/OCR-LayoutLMv3")
    # Load model directly
    from transformers import AutoProcessor, AutoModelForTokenClassification
    
    processor = AutoProcessor.from_pretrained("jinhybr/OCR-LayoutLMv3")
    model = AutoModelForTokenClassification.from_pretrained("jinhybr/OCR-LayoutLMv3")
  • Notebooks
  • Google Colab
  • Kaggle
OCR-LayoutLMv3
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  • 1 contributor
History: 12 commits
jinhybr's picture
jinhybr
Update handler.py
cda75d3 over 3 years ago
  • logs
    End of training over 3 years ago
  • .gitattributes
    1.48 kB
    initial commit over 3 years ago
  • .gitignore
    13 Bytes
    Training in progress, step 500 over 3 years ago
  • README.md
    4.29 kB
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  • config.json
    1.35 kB
    Training in progress, step 500 over 3 years ago
  • handler.py
    2.3 kB
    Update handler.py over 3 years ago
  • merges.txt
    456 kB
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  • preprocessor_config.json
    446 Bytes
    Training in progress, step 500 over 3 years ago
  • pytorch_model.bin
    501 MB
    xet
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  • special_tokens_map.json
    957 Bytes
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  • tokenizer.json
    2.11 MB
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  • tokenizer_config.json
    1.67 kB
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  • training_args.bin
    3.38 kB
    xet
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  • vocab.json
    798 kB
    Training in progress, step 500 over 3 years ago