| --- |
| 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](https://arxiv.org/abs/2111.15664) by Geewok et al. and first released in [this repository](https://github.com/clovaai/donut). |
|
|
| 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](https://huggingface.co/models?search=donut) to look for fine-tuned versions on a task that interests you. |
| |
| ### How to use |
| |
| We refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/donut) which includes code examples. |
| |
| ### BibTeX entry and citation info |
| |
| ```bibtex |
| @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} |
| } |
| ``` |
