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OCR-DocVQA-Donut
jinhybr/OCR-DocVQA-Donut
OCR-DocVQA-Donut is a document question answering model from jinhybr. Use it for the document question answering task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as mit.
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.
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At a glance
- Task
- Document Question Answering
- Library
- transformers
- License
- mit
- Model type
- vision-encoder-decoder
- Access
- Public
- Created
- Nov 4, 2022
- Updated
- Nov 4, 2022
- SHA
- 8436d13a
Try a prompt
Model card
From the Hugging Face model README
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.
- Task
- Document Question Answering
- Library
- transformers
- Type
- vision-encoder-decoder
- License
- mit
- Created
- Nov 4, 2022
- Updated
- Nov 4, 2022