Datasets:

google
/

wit

	# coding=utf-8
	# 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.
	"""Wikipedia-based Image Text (WIT) Dataset is a large multimodal multilingual dataset"""
	import csv

	import datasets


	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """\
	@article{srinivasan2021wit,
	title={WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning},
	author={Srinivasan, Krishna and Raman, Karthik and Chen, Jiecao and Bendersky, Michael and Najork, Marc},
	journal={arXiv preprint arXiv:2103.01913},
	year={2021}
	}
	"""

	# You can copy an official description
	_DESCRIPTION = """\
	Wikipedia-based Image Text (WIT) Dataset is a large multimodal multilingual dataset.
	WIT is composed of a curated set of 37.6 million entity rich image-text examples with 11.5 million unique images across 108 Wikipedia languages.
	Its size enables WIT to be used as a pretraining dataset for multimodal machine learning models.
	"""

	_HOMEPAGE = "https://github.com/google-research-datasets/wit"

	_LICENSE = "Data is available under the Creative Commons Attribution-ShareAlike 3.0 Unported license."

	_URLs = [f"https://storage.googleapis.com/gresearch/wit/wit_v1.train.all-{i:05}-of-00010.tsv.gz" for i in range(0, 10)]

	_FEATURES = datasets.Features(
	{
	"language": datasets.Value("string"),
	"page_url": datasets.Value("string"),
	"image_url": datasets.Value("string"),
	"page_title": datasets.Value("string"),
	"section_title": datasets.Value("string"),
	"hierarchical_section_title": datasets.Value("string"),
	"caption_reference_description": datasets.Value("string"),
	"caption_attribution_description": datasets.Value("string"),
	"caption_alt_text_description": datasets.Value("string"),
	"mime_type": datasets.Value("string"),
	"original_height": datasets.Value("int32"),
	"original_width": datasets.Value("int32"),
	"is_main_image": datasets.Value("bool"),
	"attribution_passes_lang_id": datasets.Value("bool"),
	"page_changed_recently": datasets.Value("bool"),
	"context_page_description": datasets.Value("string"),
	"context_section_description": datasets.Value("string"),
	}
	)


	class WIT(datasets.GeneratorBasedBuilder):
	"""Builder for WIT."""

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=_FEATURES,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	files = dl_manager.download_and_extract(_URLs)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"files": files,
	},
	),
	]

	def _generate_examples(self, files):
	idx = 0
	for file in files:
	with open(file, "r", encoding="utf-8") as f:
	examples = csv.DictReader(f, delimiter="\t")
	for example in examples:
	yield idx, {k: v if v != "" else None for k, v in example.items()}
	idx += 1