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--- |
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license: mit |
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tags: |
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- donut |
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- image-to-text |
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- vision |
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--- |
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# Donut (base-sized model, pre-trained only) |
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Donut model pre-trained-only. It was introduced in the paper [OCR-free Document Understanding Transformer](https://arxiv.org/abs/2111.15664) by Geewok et al. and first released in [this repository](https://github.com/clovaai/donut). |
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Disclaimer: The team releasing Donut did not write a model card for this model so this model card has been written by the Hugging Face team. |
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## Model description |
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Donut consists of a vision encoder (Swin Transformer) and a text decoder (BART). Given an image, the encoder first encodes the image into a tensor of embeddings (of shape batch_size, seq_len, hidden_size), after which the decoder autoregressively generates text, conditioned on the encoding of the encoder. |
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## Intended uses & limitations |
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This model is meant to be fine-tuned on a downstream task, like document image classification or document parsing. See the [model hub](https://huggingface.co/models?search=donut) to look for fine-tuned versions on a task that interests you. |
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### How to use |
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We refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/donut) which includes code examples. |
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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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``` |
