license	pipeline_tag	tags	widget
mit	document-question-answering	donut image-to-text vision	text src What is the invoice number? 2359223c18/invoice.png text src What is the purchase amount? 2359223c18/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 by Geewok et al. and first released in this repository.

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.

Intended uses & limitations

This model is fine-tuned on DocVQA, a document visual question answering dataset.

We refer to the documentation which includes code examples.