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--- |
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license: apache-2.0 |
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tags: |
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- vision |
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pipeline_tag: image-to-text |
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--- |
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# Nougat model, base-sized version |
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Nougat model trained on PDF-to-markdown. It was introduced in the paper [Nougat: Neural Optical Understanding for Academic Documents](https://arxiv.org/abs/2308.13418) by Blecher et al. and first released in [this repository](https://github.com/facebookresearch/nougat/tree/main). |
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Disclaimer: The team releasing Nougat did not write a model card for this model so this model card has been written by the Hugging Face team. |
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## Model description |
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Nougat is a [Donut](https://huggingface.co/docs/transformers/model_doc/donut) model trained to transcribe scientific PDFs into an easy-to-use markdown format. The model consists of a Swin Transformer as vision encoder, and an mBART model as text decoder. |
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The model is trained to autoregressively predict the markdown given only the pixels of the PDF image as input. |
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/nougat_architecture.jpg" |
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alt="drawing" width="600"/> |
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<small> Nougat high-level overview. Taken from the <a href="https://arxiv.org/abs/2308.13418">original paper</a>. </small> |
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## Intended uses & limitations |
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You can use the raw model for transcribing a PDF into Markdown. See the [model hub](https://huggingface.co/models?search=nougat) to look for other |
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fine-tuned versions that may interest you. |
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### How to use |
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We refer to the [docs](https://huggingface.co/docs/transformers/main/en/model_doc/nougat). |
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### BibTeX entry and citation info |
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```bibtex |
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@misc{blecher2023nougat, |
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title={Nougat: Neural Optical Understanding for Academic Documents}, |
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author={Lukas Blecher and Guillem Cucurull and Thomas Scialom and Robert Stojnic}, |
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year={2023}, |
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eprint={2308.13418}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.LG} |
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} |
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``` |
