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naver-clova-ix
/
donut-base

Image-to-Text
Transformers
PyTorch
vision-encoder-decoder
image-text-to-text
donut
vision
Model card Files Files and versions
xet
Community
16

Instructions to use naver-clova-ix/donut-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use naver-clova-ix/donut-base with Transformers:

    # Use a pipeline as a high-level helper
    # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5.
    # You must load the model directly (see below) or downgrade to v4.x with:
    # 'pip install "transformers<5.0.0'
    from transformers import pipeline
    
    pipe = pipeline("image-to-text", model="naver-clova-ix/donut-base")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForImageTextToText
    
    tokenizer = AutoTokenizer.from_pretrained("naver-clova-ix/donut-base")
    model = AutoModelForImageTextToText.from_pretrained("naver-clova-ix/donut-base")
  • Notebooks
  • Google Colab
  • Kaggle
donut-base
814 MB
Ctrl+K
Ctrl+K
  • 2 contributors
History: 7 commits
nielsr's picture
nielsr HF Staff
Update README.md
a959cf3 almost 4 years ago
  • .gitattributes
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  • .gitignore
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  • README.md
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  • added_tokens.json
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  • config.json
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  • preprocessor_config.json
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  • pytorch_model.bin

    Detected Pickle imports (4)

    • "torch.FloatStorage",
    • "torch._utils._rebuild_tensor_v2",
    • "collections.OrderedDict",
    • "torch.LongStorage"

    What is a pickle import?

    809 MB
    xet
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  • sentencepiece.bpe.model
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  • special_tokens_map.json
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  • tokenizer.json
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  • tokenizer_config.json
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