Image-to-Text
Transformers
PyTorch
Safetensors
vision-encoder-decoder
image-text-to-text
vision
nougat
Instructions to use facebook/nougat-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/nougat-small with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="facebook/nougat-small")# Load model directly from transformers import AutoTokenizer, AutoModelForImageTextToText tokenizer = AutoTokenizer.from_pretrained("facebook/nougat-small") model = AutoModelForImageTextToText.from_pretrained("facebook/nougat-small") - Notebooks
- Google Colab
- Kaggle
| license: cc-by-4.0 | |
| tags: | |
| - vision | |
| - nougat | |
| pipeline_tag: image-to-text | |
| # Nougat model, small-sized version | |
| 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). | |
| 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. | |
| Note: this model corresponds to the "0.1.0-small" version of the original repository. | |
| ## Model description | |
| 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. | |
| The model is trained to autoregressively predict the markdown given only the pixels of the PDF image as input. | |
| <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/nougat_architecture.jpg" | |
| alt="drawing" width="600"/> | |
| <small> Nougat high-level overview. Taken from the <a href="https://arxiv.org/abs/2308.13418">original paper</a>. </small> | |
| ## Intended uses & limitations | |
| 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 | |
| fine-tuned versions that may interest you. | |
| ### How to use | |
| We refer to the [docs](https://huggingface.co/docs/transformers/main/en/model_doc/nougat). | |
| ### BibTeX entry and citation info | |
| ```bibtex | |
| @misc{blecher2023nougat, | |
| title={Nougat: Neural Optical Understanding for Academic Documents}, | |
| author={Lukas Blecher and Guillem Cucurull and Thomas Scialom and Robert Stojnic}, | |
| year={2023}, | |
| eprint={2308.13418}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.LG} | |
| } | |
| ``` |