Datasets:
Languages:
English
Multilinguality:
monolingual
Size Categories:
100K<n<1M
Language Creators:
machine-generated
Annotations Creators:
machine-generated
Source Datasets:
original
ArXiv:
License:
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
"""DocBank document understanding dataset.""" | |
import os | |
import datasets | |
# Find for instance the citation on arxiv or on the dataset repo/website | |
_CITATION = """\ | |
@misc{li2020docbank, | |
title={DocBank: A Benchmark Dataset for Document Layout Analysis}, | |
author={Minghao Li and Yiheng Xu and Lei Cui and Shaohan Huang and Furu Wei and Zhoujun Li and Ming Zhou}, | |
year={2020}, | |
eprint={2006.01038}, | |
archivePrefix={arXiv}, | |
primaryClass={cs.CL} | |
} | |
""" | |
# You can copy an official description | |
_DESCRIPTION = """\ | |
DocBank is a new large-scale dataset that is constructed using a weak supervision approach. | |
It enables models to integrate both the textual and layout information for downstream tasks. | |
The current DocBank dataset totally includes 500K document pages, where 400K for training, 50K for validation and 50K for testing. | |
""" | |
_HOMEPAGE = "https://doc-analysis.github.io/docbank-page/index.html" | |
_LICENSE = "Apache-2.0 license" | |
class DocBank(datasets.GeneratorBasedBuilder): | |
"""DocBank is a dataset for Visual Document Understanding. | |
It enable models to integrate both textual and layout informtion for downstream tasks.""" | |
VERSION = datasets.Version("1.1.0") | |
def manual_download_instructions(self): | |
return """\ | |
Please download the DocBank dataset from https://doc-analysis.github.io/docbank-page/index.html. Uncompress the dataset and use that location in | |
--data_dir argument. """ | |
def _info(self): | |
features = datasets.Features( | |
{ | |
"image": datasets.Image(), | |
"token": datasets.Value("string"), | |
"bounding_box": datasets.Sequence(datasets.Sequence(datasets.Value("uint16"))), | |
"color": datasets.Sequence(datasets.Sequence(datasets.Value("uint8"))), | |
"font": datasets.Value("string"), | |
"label": datasets.Value("string"), | |
} | |
) | |
return datasets.DatasetInfo( | |
# This is the description that will appear on the datasets page. | |
description=_DESCRIPTION, | |
# This defines the different columns of the dataset and their types | |
features=features, # Here we define them above because they are different between the two configurations | |
# If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and | |
# specify them. They'll be used if as_supervised=True in builder.as_dataset. | |
# supervised_keys=("sentence", "label"), | |
# Homepage of the dataset for documentation | |
homepage=_HOMEPAGE, | |
# License for the dataset if available | |
license=_LICENSE, | |
# Citation for the dataset | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration | |
# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name | |
# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS | |
# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files. | |
# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive | |
# urls = _URLS[self.config.name] | |
# data_dir = dl_manager.download_and_extract(urls) | |
self.data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir)) | |
cwd = os.path.dirname(os.path.abspath(__file__)) | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={ | |
"filepath": os.path.join(cwd,"train.jsonl"), | |
"split": "train", | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.VALIDATION, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={ | |
"filepath": os.path.join(cwd,"dev.jsonl"), | |
"split": "dev", | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={ | |
"filepath": os.path.join(cwd,"test.jsonl"), | |
"split": "test" | |
}, | |
), | |
] | |
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators` | |
def _generate_examples(self, filepath, split): | |
with open(filepath,'rt') as fp: | |
for file in fp: | |
index,basename = eval(file) | |
txt_file = self.data_dir+'/DocBank_500K_txt/'+basename+'.txt' | |
img_file = self.data_dir+'/DocBank_500K_ori_img/'+basename+'_ori.jpg' | |
with open(txt_file, 'r', encoding='utf8') as fp: | |
words = [] | |
bboxes = [] | |
rgbs = [] | |
fontnames = [] | |
structures = [] | |
for row in fp: | |
tts = row.split('\t') | |
assert len(tts) == 10, f'Incomplete line in file {txt_file}' | |
word = tts[0] | |
bbox = list(map(int, tts[1:5])) | |
rgb = list(map(int, tts[5:8])) | |
fontname = tts[8] | |
structure = tts[9].strip() | |
words.append(word) | |
bboxes.append(bbox) | |
rgbs.append(rgb) | |
fontnames.append(fontname) | |
structures.append(structure) | |
# index = str(index)+'_'+str(row) | |
yield index, { | |
"image": img_file, | |
"token": words, | |
"bounding_box": bboxes, | |
"color": rgbs, | |
"font": fontnames, | |
"label": structures, | |
} |