Document Question Answering
Transformers
Safetensors
Vietnamese
vision-encoder-decoder
image-text-to-text
Instructions to use YuukiAsuna/VieTable-donut-docvqa-demo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YuukiAsuna/VieTable-donut-docvqa-demo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="YuukiAsuna/VieTable-donut-docvqa-demo")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("YuukiAsuna/VieTable-donut-docvqa-demo") model = AutoModelForMultimodalLM.from_pretrained("YuukiAsuna/VieTable-donut-docvqa-demo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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<!-- Provide a quick summary of what the model is/does. -->
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VieTable Donut DocVQA is a fine-tuned version of the Donut model for the Vietnamese DocVQA (Table data)
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<!-- Provide a quick summary of what the model is/does. -->
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VieTable Donut DocVQA is a fine-tuned version of the Donut model for the Vietnamese DocVQA (Table data)
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### BibTeX entry and citation info
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```bibtex
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@article{DBLP:journals/corr/abs-2111-15664,
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author = {Geewook Kim and
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Teakgyu Hong and
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Moonbin Yim and
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Jinyoung Park and
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Jinyeong Yim and
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Wonseok Hwang and
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Sangdoo Yun and
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Dongyoon Han and
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Seunghyun Park},
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title = {Donut: Document Understanding Transformer without {OCR}},
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journal = {CoRR},
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volume = {abs/2111.15664},
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year = {2021},
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url = {https://arxiv.org/abs/2111.15664},
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eprinttype = {arXiv},
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eprint = {2111.15664},
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timestamp = {Thu, 02 Dec 2021 10:50:44 +0100},
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biburl = {https://dblp.org/rec/journals/corr/abs-2111-15664.bib},
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bibsource = {dblp computer science bibliography, https://dblp.org}
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}
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```
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