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transformers/docs/source/en/model_doc/nougat.md

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+*This model was released on 2023-08-25 and added to Hugging Face Transformers on 2023-09-26.*
+
+# Nougat
+
+<div class="flex flex-wrap space-x-1">
+<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
+</div>
+
+## Overview
+
+The Nougat model was proposed in [Nougat: Neural Optical Understanding for Academic Documents](https://huggingface.co/papers/2308.13418) by
+Lukas Blecher, Guillem Cucurull, Thomas Scialom, Robert Stojnic. Nougat uses the same architecture as [Donut](donut), meaning an image Transformer
+encoder and an autoregressive text Transformer decoder to translate scientific PDFs to markdown, enabling easier access to them.
+
+The abstract from the paper is the following:
+
+*Scientific knowledge is predominantly stored in books and scientific journals, often in the form of PDFs. However, the PDF format leads to a loss of semantic information, particularly for mathematical expressions. We propose Nougat (Neural Optical Understanding for Academic Documents), a Visual Transformer model that performs an Optical Character Recognition (OCR) task for processing scientific documents into a markup language, and demonstrate the effectiveness of our model on a new dataset of scientific documents. The proposed approach offers a promising solution to enhance the accessibility of scientific knowledge in the digital age, by bridging the gap between human-readable documents and machine-readable text. We release the models and code to accelerate future work on scientific text recognition.*
+
+<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://huggingface.co/papers/2308.13418">original paper</a>. </small>
+
+This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found
+[here](https://github.com/facebookresearch/nougat).
+
+## Usage tips
+
+- The quickest way to get started with Nougat is by checking the [tutorial
+  notebooks](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/Nougat), which show how to use the model
+  at inference time as well as fine-tuning on custom data.
+- Nougat is always used within the [VisionEncoderDecoder](vision-encoder-decoder) framework. The model is identical to [Donut](donut) in terms of architecture.
+
+## Inference
+
+Nougat's [`VisionEncoderDecoder`] model accepts images as input and makes use of
+[`~generation.GenerationMixin.generate`] to autoregressively generate text given the input image.
+
+The [`NougatImageProcessor`] class is responsible for preprocessing the input image and
+[`NougatTokenizerFast`] decodes the generated target tokens to the target string. The
+[`NougatProcessor`] wraps [`NougatImageProcessor`] and [`NougatTokenizerFast`] classes
+into a single instance to both extract the input features and decode the predicted token ids.
+
+- Step-by-step PDF transcription
+
+```py
+>>> from huggingface_hub import hf_hub_download
+>>> import re
+>>> from PIL import Image
+
+>>> from transformers import NougatProcessor, VisionEncoderDecoderModel, infer_device
+>>> from datasets import load_dataset
+>>> import torch
+
+>>> processor = NougatProcessor.from_pretrained("facebook/nougat-base")
+>>> model = VisionEncoderDecoderModel.from_pretrained("facebook/nougat-base")
+
+>>> device = infer_device()
+>>> model.to(device)  # doctest: +IGNORE_RESULT
+
+>>> # prepare PDF image for the model
+>>> filepath = hf_hub_download(repo_id="hf-internal-testing/fixtures_docvqa", filename="nougat_paper.png", repo_type="dataset")
+>>> image = Image.open(filepath)
+>>> pixel_values = processor(image, return_tensors="pt").pixel_values
+
+>>> # generate transcription (here we only generate 30 tokens)
+>>> outputs = model.generate(
+...     pixel_values.to(device),
+...     min_length=1,
+...     max_new_tokens=30,
+...     bad_words_ids=[[processor.tokenizer.unk_token_id]],
+... )
+
+>>> sequence = processor.batch_decode(outputs, skip_special_tokens=True)[0]
+>>> sequence = processor.post_process_generation(sequence, fix_markdown=False)
+>>> # note: we're using repr here such for the sake of printing the \n characters, feel free to just print the sequence
+>>> print(repr(sequence))
+'\n\n# Nougat: Neural Optical Understanding for Academic Documents\n\n Lukas Blecher\n\nCorrespondence to: lblecher@'
+```
+
+See the [model hub](https://huggingface.co/models?filter=nougat) to look for Nougat checkpoints.
+
+<Tip>
+
+The model is identical to [Donut](donut) in terms of architecture.
+
+</Tip>
+
+## NougatImageProcessor
+
+[[autodoc]] NougatImageProcessor
+    - preprocess
+
+## NougatImageProcessorFast
+
+[[autodoc]] NougatImageProcessorFast
+    - preprocess
+
+## NougatTokenizerFast
+
+[[autodoc]] NougatTokenizerFast
+
+## NougatProcessor
+
+[[autodoc]] NougatProcessor
+    - __call__
+    - from_pretrained
+    - save_pretrained
+    - batch_decode
+    - decode
+    - post_process_generation