The Nougat model was proposed in Nougat: Neural Optical Understanding for Academic Documents by Lukas Blecher, Guillem Cucurull, Thomas Scialom, Robert Stojnic. Nougat uses the same architecture as 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.
This model was contributed by nielsr. The original code can be found here.
Nougat’s VisionEncoderDecoder
model accepts images as input and makes use of
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.
>>> from huggingface_hub import hf_hub_download
>>> import re
>>> from PIL import Image
>>> from transformers import NougatProcessor, VisionEncoderDecoderModel
>>> from datasets import load_dataset
>>> import torch
>>> processor = NougatProcessor.from_pretrained("facebook/nougat-base")
>>> model = VisionEncoderDecoderModel.from_pretrained("facebook/nougat-base")
>>> device = "cuda" if torch.cuda.is_available() else "cpu"
>>> model.to(device)
>>> # 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 to look for Nougat checkpoints.
The model is identical to Donut in terms of architecture.
( do_crop_margin: bool = True do_resize: bool = True size: Dict = None resample: Resampling = <Resampling.BILINEAR: 2> do_thumbnail: bool = True do_align_long_axis: bool = False do_pad: bool = True do_rescale: bool = True rescale_factor: Union = 0.00392156862745098 do_normalize: bool = True image_mean: Union = None image_std: Union = None **kwargs )
Parameters
bool
, optional, defaults to True
) —
Whether to crop the image margins. bool
, optional, defaults to True
) —
Whether to resize the image’s (height, width) dimensions to the specified size
. Can be overridden by
do_resize
in the preprocess
method. Dict[str, int]
optional, defaults to {"height" -- 896, "width": 672}
):
Size of the image after resizing. Can be overridden by size
in the preprocess
method. PILImageResampling
, optional, defaults to Resampling.BILINEAR
) —
Resampling filter to use if resizing the image. Can be overridden by resample
in the preprocess
method. bool
, optional, defaults to True
) —
Whether to resize the image using thumbnail method. bool
, optional, defaults to False
) —
Whether to align the long axis of the image with the long axis of size
by rotating by 90 degrees. bool
, optional, defaults to True
) —
Whether to pad the images to the largest image size in the batch. bool
, optional, defaults to True
) —
Whether to rescale the image by the specified scale rescale_factor
. Can be overridden by the do_rescale
parameter in the preprocess
method. int
or float
, optional, defaults to 1/255
) —
Scale factor to use if rescaling the image. Can be overridden by the rescale_factor
parameter in the
preprocess
method. bool
, optional, defaults to True
) —
Whether to normalize the image. Can be overridden by do_normalize
in the preprocess
method. float
or List[float]
, optional, defaults to IMAGENET_DEFAULT_MEAN
) —
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
channels in the image. Can be overridden by the image_mean
parameter in the preprocess
method. float
or List[float]
, optional, defaults to IMAGENET_DEFAULT_STD
) —
Image standard deviation. Constructs a Nougat image processor.
( images: Union do_crop_margin: bool = None do_resize: bool = None size: Dict = None resample: Resampling = None do_thumbnail: bool = None do_align_long_axis: bool = None do_pad: bool = None do_rescale: bool = None rescale_factor: Union = None do_normalize: bool = None image_mean: Union = None image_std: Union = None return_tensors: Union = None data_format: Optional = <ChannelDimension.FIRST: 'channels_first'> input_data_format: Union = None **kwargs )
Parameters
ImageInput
) —
Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. bool
, optional, defaults to self.do_crop_margin
) —
Whether to crop the image margins. bool
, optional, defaults to self.do_resize
) —
Whether to resize the image. Dict[str, int]
, optional, defaults to self.size
) —
Size of the image after resizing. Shortest edge of the image is resized to min(size[“height”],
size[“width”]) with the longest edge resized to keep the input aspect ratio. int
, optional, defaults to self.resample
) —
Resampling filter to use if resizing the image. This can be one of the enum PILImageResampling
. Only
has an effect if do_resize
is set to True
. bool
, optional, defaults to self.do_thumbnail
) —
Whether to resize the image using thumbnail method. bool
, optional, defaults to self.do_align_long_axis
) —
Whether to align the long axis of the image with the long axis of size
by rotating by 90 degrees. bool
, optional, defaults to self.do_pad
) —
Whether to pad the images to the largest image size in the batch. bool
, optional, defaults to self.do_rescale
) —
Whether to rescale the image by the specified scale rescale_factor
. int
or float
, optional, defaults to self.rescale_factor
) —
Scale factor to use if rescaling the image. bool
, optional, defaults to self.do_normalize
) —
Whether to normalize the image. float
or List[float]
, optional, defaults to self.image_mean
) —
Image mean to use for normalization. float
or List[float]
, optional, defaults to self.image_std
) —
Image standard deviation to use for normalization. str
or TensorType
, optional) —
The type of tensors to return. Can be one of:np.ndarray
.TensorType.TENSORFLOW
or 'tf'
: Return a batch of type tf.Tensor
.TensorType.PYTORCH
or 'pt'
: Return a batch of type torch.Tensor
.TensorType.NUMPY
or 'np'
: Return a batch of type np.ndarray
.TensorType.JAX
or 'jax'
: Return a batch of type jax.numpy.ndarray
.ChannelDimension
or str
, optional, defaults to ChannelDimension.FIRST
) —
The channel dimension format for the output image. Can be one of:ChannelDimension.FIRST
: image in (num_channels, height, width) format.ChannelDimension.LAST
: image in (height, width, num_channels) format.ChannelDimension
or str
, optional) —
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:"channels_first"
or ChannelDimension.FIRST
: image in (num_channels, height, width) format."channels_last"
or ChannelDimension.LAST
: image in (height, width, num_channels) format."none"
or ChannelDimension.NONE
: image in (height, width) format.Preprocess an image or batch of images.
( vocab_file = None tokenizer_file = None clean_up_tokenization_spaces = False unk_token = '<unk>' bos_token = '<s>' eos_token = '</s>' pad_token = '<pad>' **kwargs )
Parameters
str
, optional) —
SentencePiece file (generally has a .model extension) that
contains the vocabulary necessary to instantiate a tokenizer. str
, optional) —
tokenizers file (generally has a .json extension) that
contains everything needed to load the tokenizer. str
, optional, defaults to False
) —
Wether to cleanup spaces after decoding, cleanup consists in removing potential artifacts like extra
spaces. str
, optional, defaults to "<unk>"
) —
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead. str
, optional, defaults to "<s>"
) —
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. str
, optional, defaults to "</s>"
) —
The end of sequence token. str
, optional, defaults to "<pad>"
) —
The token used for padding, for example when batching sequences of different lengths. int
, optional) —
The maximum length (in number of tokens) for the inputs to the transformer model. When the tokenizer is
loaded with from_pretrained(), this will be set to the
value stored for the associated model in max_model_input_sizes
(see above). If no value is provided, will
default to VERY_LARGE_INTEGER (int(1e30)
). str
, optional) —
The side on which the model should have padding applied. Should be selected between [‘right’, ‘left’].
Default value is picked from the class attribute of the same name. str
, optional) —
The side on which the model should have truncation applied. Should be selected between [‘right’, ‘left’].
Default value is picked from the class attribute of the same name. str
, optional) —
A Jinja template string that will be used to format lists of chat messages. See
https://huggingface.co/docs/transformers/chat_templating for a full description. List[string]
, optional) —
The list of inputs accepted by the forward pass of the model (like "token_type_ids"
or
"attention_mask"
). Default value is picked from the class attribute of the same name. str
or tokenizers.AddedToken
, optional) —
A special token representing the beginning of a sentence. Will be associated to self.bos_token
and
self.bos_token_id
. str
or tokenizers.AddedToken
, optional) —
A special token representing the end of a sentence. Will be associated to self.eos_token
and
self.eos_token_id
. str
or tokenizers.AddedToken
, optional) —
A special token representing an out-of-vocabulary token. Will be associated to self.unk_token
and
self.unk_token_id
. str
or tokenizers.AddedToken
, optional) —
A special token separating two different sentences in the same input (used by BERT for instance). Will be
associated to self.sep_token
and self.sep_token_id
. str
or tokenizers.AddedToken
, optional) —
A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by
attention mechanisms or loss computation. Will be associated to self.pad_token
and self.pad_token_id
. str
or tokenizers.AddedToken
, optional) —
A special token representing the class of the input (used by BERT for instance). Will be associated to
self.cls_token
and self.cls_token_id
. str
or tokenizers.AddedToken
, optional) —
A special token representing a masked token (used by masked-language modeling pretraining objectives, like
BERT). Will be associated to self.mask_token
and self.mask_token_id
. str
or tokenizers.AddedToken
, optional) —
A tuple or a list of additional special tokens. Add them here to ensure they are skipped when decoding with
skip_special_tokens
is set to True. If they are not part of the vocabulary, they will be added at the end
of the vocabulary. bool
, optional, defaults to True
) —
Whether or not the model should cleanup the spaces that were added when splitting the input text during the
tokenization process. bool
, optional, defaults to False
) —
Whether or not the special tokens should be split during the tokenization process. The default behavior is
to not split special tokens. This means that if <s>
is the bos_token
, then tokenizer.tokenize("<s>") = ['<s>
]. Otherwise, if split_special_tokens=True
, then tokenizer.tokenize("<s>")
will be give ['<', 's', '>']
. This argument is only supported for slow
tokenizers for the moment. str
) —
A path to a local JSON file representing a previously serialized tokenizers.Tokenizer object from 🤗
tokenizers. Fast tokenizer for Nougat (backed by HuggingFace tokenizers library).
This tokenizer inherits from PreTrainedTokenizerFast which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. This class mainly adds Nougat-specific methods for postprocessing the generated text.
Class attributes (overridden by derived classes)
Dict[str, str]
) — A dictionary with, as keys, the __init__
keyword name of each
vocabulary file required by the model, and as associated values, the filename for saving the associated file
(string).Dict[str, Dict[str, str]]
) — A dictionary of dictionaries, with the
high-level keys being the __init__
keyword name of each vocabulary file required by the model, the
low-level being the short-cut-names
of the pretrained models with, as associated values, the url
to the
associated pretrained vocabulary file.List[str]
) — A list of inputs expected in the forward pass of the model.str
) — The default value for the side on which the model should have padding applied.
Should be 'right'
or 'left'
.str
) — The default value for the side on which the model should have truncation
applied. Should be 'right'
or 'left'
.( generation: str ) → str
Takes a generated string and fixes tables/tabulars to make them match the markdown format needed.
( generation: Union fix_markdown: bool = True num_workers: int = None ) → Union[str, List[str]]
Parameters
bool
, optional, defaults to True
) —
Whether to perform Markdown formatting fixes. int
, optional) —
Optional number of workers to pass to leverage multiprocessing (postprocessing several texts in
parallel). Returns
Union[str, List[str]]
The postprocessed text or list of postprocessed texts.
Postprocess a generated text or a list of generated texts.
This function can be used to perform postprocessing on generated text, such as fixing Markdown formatting.
Postprocessing is quite slow so it is recommended to use multiprocessing to speed up the process.
( generation: str fix_markdown: bool = True ) → str
Postprocess a single generated text. Regular expressions used here are taken directly from the Nougat article authors. These expressions are commented for clarity and tested end-to-end in most cases.
( text: str ) → str
Remove hallucinated or missing references from the text.
This function identifies and removes references that are marked as missing or hallucinated from the input text.
( image_processor tokenizer )
Parameters
Constructs a Nougat processor which wraps a Nougat image processor and a Nougat tokenizer into a single processor.
NougatProcessor offers all the functionalities of NougatImageProcessor and NougatTokenizerFast. See the call() and decode() for more information.
( images = None text = None do_crop_margin: bool = None do_resize: bool = None size: Dict = None resample: PILImageResampling = None do_thumbnail: bool = None do_align_long_axis: bool = None do_pad: bool = None do_rescale: bool = None rescale_factor: Union = None do_normalize: bool = None image_mean: Union = None image_std: Union = None data_format: Optional = 'channels_first' input_data_format: Union = None text_pair: Union = None text_target: Union = None text_pair_target: Union = None add_special_tokens: bool = True padding: Union = False truncation: Union = None max_length: Optional = None stride: int = 0 is_split_into_words: bool = False pad_to_multiple_of: Optional = None return_tensors: Union = None return_token_type_ids: Optional = None return_attention_mask: Optional = None return_overflowing_tokens: bool = False return_special_tokens_mask: bool = False return_offsets_mapping: bool = False return_length: bool = False verbose: bool = True )
( pretrained_model_name_or_path: Union cache_dir: Union = None force_download: bool = False local_files_only: bool = False token: Union = None revision: str = 'main' **kwargs )
Parameters
str
or os.PathLike
) —
This can be either:
./my_model_directory/
../my_model_directory/preprocessor_config.json
.
**kwargs —
Additional keyword arguments passed along to both
from_pretrained() and
~tokenization_utils_base.PreTrainedTokenizer.from_pretrained
.Instantiate a processor associated with a pretrained model.
This class method is simply calling the feature extractor
from_pretrained(), image processor
ImageProcessingMixin and the tokenizer
~tokenization_utils_base.PreTrainedTokenizer.from_pretrained
methods. Please refer to the docstrings of the
methods above for more information.
( save_directory push_to_hub: bool = False **kwargs )
Parameters
str
or os.PathLike
) —
Directory where the feature extractor JSON file and the tokenizer files will be saved (directory will
be created if it does not exist). bool
, optional, defaults to False
) —
Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the
repository you want to push to with repo_id
(will default to the name of save_directory
in your
namespace). Dict[str, Any]
, optional) —
Additional key word arguments passed along to the push_to_hub() method. Saves the attributes of this processor (feature extractor, tokenizer…) in the specified directory so that it can be reloaded using the from_pretrained() method.
This class method is simply calling save_pretrained() and save_pretrained(). Please refer to the docstrings of the methods above for more information.
This method forwards all its arguments to NougatTokenizer’s batch_decode(). Please refer to the docstring of this method for more information.
This method forwards all its arguments to NougatTokenizer’s decode(). Please refer to the docstring of this method for more information.
This method forwards all its arguments to NougatTokenizer’s ~PreTrainedTokenizer.post_process_generation
.
Please refer to the docstring of this method for more information.