readme: ---
  license: mit
  tags:
  - donut
  - image-to-text
  - vision
  ---
  
  # Donut (base-sized model, pre-trained only) 
  
  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).
  
  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.
  
  ## Model description
  
  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. 
  
  ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/donut_architecture.jpg)
  
  ## Intended uses & limitations
  
  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.
  
  ### How to use
  
  We refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/donut) which includes code examples.
  
  ### BibTeX entry and citation info
  
  ```bibtex
  @article{DBLP:journals/corr/abs-2111-15664,
    author    = {Geewook Kim and
                 Teakgyu Hong and
                 Moonbin Yim and
                 Jinyoung Park and
                 Jinyeong Yim and
                 Wonseok Hwang and
                 Sangdoo Yun and
                 Dongyoon Han and
                 Seunghyun Park},
    title     = {Donut: Document Understanding Transformer without {OCR}},
    journal   = {CoRR},
    volume    = {abs/2111.15664},
    year      = {2021},
    url       = {https://arxiv.org/abs/2111.15664},
    eprinttype = {arXiv},
    eprint    = {2111.15664},
    timestamp = {Thu, 02 Dec 2021 10:50:44 +0100},
    biburl    = {https://dblp.org/rec/journals/corr/abs-2111-15664.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  ```