Datasets:
Sub-tasks:
image-captioning
Multilinguality:
multilingual
Size Categories:
10M<n<100M
Language Creators:
found
Annotations Creators:
machine-generated
ArXiv:
Tags:
License:
File size: 3,808 Bytes
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# 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
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