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aftdb.py
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import os
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import json
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import datasets
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from PIL import Image
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_DESCRIPTION = """
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The Arxiv Figure Table Database (AFTdb) facilitates the linking of documentary
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objects, such as figures and tables, with their captions. This enables a
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comprehensive description of document-oriented images (excluding images from
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cameras). For the table component, the character structure is preserved in
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addition to the image of the table and its caption. This database is ideal
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for multimodal processing of documentary images.
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"""
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_LICENSE = "apache-2.0"
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_CITATION = """
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@online{AFTdb,
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AUTHOR = {Cyrile Delestre},
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URL = {},
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YEAR = {2024},
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KEYWORDS = {NLP ; Multimodal}
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}
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"""
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_NB_TAR_FIGURE = [158, 4] # train, test
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_NB_TAR_TABLE = [17, 1] # train, test
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def extract_files_tar(all_path, data_dir, nb_files):
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paths = [
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os.path.join(data_dir, f"train-{ii:03d}.tar")
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for ii in range(nb_files[0])
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]
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all_path['train'] += paths
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paths = [
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os.path.join(data_dir, f"test-{ii:03d}.tar")
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for ii in range(nb_files[1])
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]
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all_path['test'] += paths
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class AFTConfig(datasets.BuilderConfig):
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"""Builder Config for AFT"""
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def __init__(self, nb_files_figure, nb_files_table, **kwargs):
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"""BuilderConfig for Food-101.
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Args:
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data_url: `string`, url to download the zip file from.
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"""
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super().__init__(version=datasets.__version__, **kwargs)
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self.nb_files_figure = nb_files_figure
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self.nb_files_table = nb_files_table
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class AFT_Dataset(datasets.GeneratorBasedBuilder):
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"""Arxiv Figure Table (AFT) dataset"""
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BUILDER_CONFIGS = [
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AFTConfig(
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name="figure",
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description=(
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"Dataset containing scientific article figures associated "
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"with their caption, summary, and article title."
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),
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data_dir="./data/arxiv_dataset/{type}",
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nb_files_figure=_NB_TAR_FIGURE,
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nb_files_table=None
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),
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AFTConfig(
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name="table",
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description=(
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"Dataset containing tables in JPG image format from "
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"scientific articles, along with the corresponding textual "
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"representation of the table, including its caption, summary, "
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"and article title."
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),
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data_dir="./data/arxiv_dataset/{type}",
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nb_files_figure=None,
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nb_files_table=_NB_TAR_TABLE
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),
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AFTConfig(
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name="figure+table",
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description=(
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"Dataset containing figure and tables in JPG image format "
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"from scientific articles, along with the corresponding "
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"textual representation of the table, including its caption, "
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"summary, and article title."
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),
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data_dir="./data/arxiv_dataset/{type}",
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nb_files_figure=_NB_TAR_FIGURE,
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nb_files_table=_NB_TAR_TABLE
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)
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]
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DEFAULT_CONFIG_NAME = "figure+table"
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=datasets.Features(
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{
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'id': datasets.Value('string'),
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'paper_id': datasets.Value('string'),
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'type': datasets.Value('string'),
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'authors': datasets.Value('string'),
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'categories': datasets.Value('string'),
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'title': {
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'english': datasets.Value('string'),
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'french': datasets.Value('string')
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},
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'summary': {
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'english': datasets.Value('string'),
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'french': datasets.Value('string')
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},
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'caption': {
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'english': datasets.Value('string'),
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'french': datasets.Value('string')
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},
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'image': datasets.Image(),
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'data': datasets.Value('string'),
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'newcommands': datasets.Sequence(datasets.Value('string'))
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}
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),
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citation=_CITATION,
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license=_LICENSE
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)
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def _split_generators(self, dl_manager: datasets.DownloadManager):
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all_path = dict(train=[], test=[])
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if self.config.nb_files_figure:
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extract_files_tar(
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all_path=all_path,
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data_dir=self.config.data_dir.format(type='figure'),
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nb_files=self.config.nb_files_figure
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)
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if self.config.nb_files_table:
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extract_files_tar(
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all_path=all_path,
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data_dir=self.config.data_dir.format(type='table'),
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nb_files=self.config.nb_files_table
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)
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if dl_manager.is_streaming:
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downloaded_files = dl_manager.download(all_path)
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else:
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downloaded_files = dl_manager.download_and_extract(all_path)
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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'filepaths': downloaded_files['train'],
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'is_streaming': dl_manager.is_streaming
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}
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={
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"filepaths": downloaded_files['test'],
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'is_streaming': dl_manager.is_streaming
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}
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)
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]
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+
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def _generate_examples(self, filepaths, is_streaming):
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if is_streaming:
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_json, _jpg = False, False
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dl_manager = datasets.DownloadManager()
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+
for path_tar in filepaths:
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iter_tar = dl_manager.iter_archive(path_tar)
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+
for path, file_obj in iter_tar:
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if path.endswith('.json'):
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metadata = json.load(file_obj)
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+
_json = True
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if path.endswith('.jpg'):
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img = Image.open(file_obj)
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_jpg = True
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if _json and _jpg:
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_json, _jpg = False, False
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+
yield metadata['id'], {
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'id': metadata['id'],
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'paper_id': metadata['paper_id'],
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'type': metadata['type'],
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+
'authors': metadata['authors'],
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'categories': metadata['categories'],
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'title': metadata['title'],
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'summary': metadata['summary'],
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'caption': metadata['caption'],
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'image': img,
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'data': metadata['data'],
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'newcommands': metadata['newcommands']
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}
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else:
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for path in filepaths:
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all_file = os.listdir(path)
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all_id_obs = sorted(
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set(map(lambda x: x.split('.')[0], all_file))
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)
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for id_obs in all_id_obs:
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path_metadata = os.path.join(
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+
path,
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f"{id_obs}.metadata.json"
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)
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path_image = os.path.join(path, f"{id_obs}.image.jpg")
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+
metadata = json.load(open(path_metadata, 'r'))
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+
img = Image.open(path_image)
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+
yield id_obs, {
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+
'id': metadata['id'],
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'paper_id': metadata['paper_id'],
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+
'type': metadata['type'],
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+
'authors': metadata['authors'],
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+
'categories': metadata['categories'],
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'title': metadata['title'],
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'summary': metadata['summary'],
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'caption': metadata['caption'],
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'image': img,
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'data': metadata['data'],
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'newcommands': metadata['newcommands']
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+
}
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