OCR DocVQA Donut

🚀 Donut (base-sized model, fine-tuned on DocVQA)

A Donut model fine-tuned on DocVQA, designed for document question answering.

🚀 Quick Start

This Donut model is 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.

✨ Features

• Document Question Answering: Capable of answering questions based on document images.
• OCR-free: Utilizes a vision encoder and text decoder to process documents without traditional OCR.

📚 Documentation

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.

📄 License

This model is released under the MIT license.