license: mit
tags:
- legal
- agent
datasets:
- TeichAI/claude-4.5-opus-high-reasoning-250x
language:
- ae
- vi
- en
metrics:
- accuracy
- character
base_model:
- naver-clova-ix/donut-base
new_version: titantv090/donut-base-2.5
library_name: fastai
Donut (base-sized model, pre-trained only)
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.
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 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.
How to use
We refer to the documentation which includes code examples.
BibTeX entry and citation info
@article{DBLP:journals/corr/abs-2111-15664,
author = {Geewook Kim and
Teakgyu Hong and
Moonbin Yim and
Jinyoung Park and
Jinyeong Yim and
Wonseok Hwang and
Sangdoo Yun and
Dongyoon Han and
Seunghyun Park},
title = {Donut: Document Understanding Transformer without {OCR}},
journal = {CoRR},
volume = {abs/2111.15664},
year = {2021},
url = {https://arxiv.org/abs/2111.15664},
eprinttype = {arXiv},
eprint = {2111.15664},
timestamp = {Thu, 02 Dec 2021 10:50:44 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2111-15664.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}