File size: 3,782 Bytes
33268ec 8a6ee80 33268ec 8a6ee80 33268ec f669f4f 33268ec | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 | import json
import datasets
from datasets import Value, Sequence, Features
_CITATION = """\\n@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}
}
"""
_DESCRIPTION = """\\nWikipedia-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"
_URL = "https://storage.googleapis.com/huggingface-nlp/datasets/wit/"
_URLS = {
# TODO - This should be in range(400). Haven't mirrored all the files yet.
'train': [_URL + f"part-{'%05d' % i}-48a6f07e-bb86-4735-aac7-883349f41a28-c000.json.gz" for i in range(10)]
}
class Wit(datasets.GeneratorBasedBuilder):
"""WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning"""
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=Features({
'b64_bytes': Value('string'),
'embedding': Sequence(Value('float64')),
'image_url': Value('string'),
'metadata_url': Value('string'),
'wit_features': Sequence({
"language": Value('string'),
"page_url": Value('string'),
"image_url": Value('string'),
"attribution_passes_lang_id": Value("string"),
"caption_alt_text_description": Value('string'),
"caption_attribution_description": Value('string'),
"caption_reference_description": Value('string'),
"caption_title_and_reference_description": Value('string'),
"context_page_description": Value('string'),
"context_section_description": Value('string'),
"hierarchical_section_title": Value('string'),
"is_main_image": Value('string'),
"mime_type": Value('string'),
"original_height": Value('string'),
"original_width": Value('string'),
"page_changed_recently": Value('string'),
"page_title": Value('string'),
"section_title": Value('string'),
})
}),
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
urls_to_download = _URLS
downloaded_files = dl_manager.download_and_extract(urls_to_download)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": downloaded_files["train"]}),
]
def _generate_examples(self, filepaths):
"""Yields examples."""
wit_feature_names = self.info.features['wit_features'].feature.keys()
for filepath in filepaths:
with open(filepath, "rb") as f:
for i, line in enumerate(f):
line = line.strip()
row_data = json.loads(line, encoding='utf-8')
for feature in row_data['wit_features']:
for fname in wit_feature_names:
if fname not in feature:
feature[fname] = None
yield str(i), row_data
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