readme: ---
  license: mit
  pipeline_tag: document-question-answering
  tags:
  - donut
  - image-to-text
  - vision
  widget:
  - text: "What is the invoice number?"
    src: "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"
  - text: "What is the purchase amount?"
    src: "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/contract.jpeg"
  ---
  
  # Donut (base-sized model, fine-tuned on DocVQA) 
  
  Donut model fine-tuned on DocVQA. 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 fine-tuned on DocVQA, a document visual question answering dataset.
  
  We refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/donut) which includes code examples.