Donut model pre-trained only. 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.

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.

This model is meant to be fine-tuned on a downstream task, like document image classification or document parsing. See the model hub to look for fine-tuned versions on a task that interests you.

We refer to the documentation which includes code examples.