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import json
import datasets
from datasets import Features, Sequence, Value
_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}
}
"""
_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"
_URL = "https://storage.googleapis.com/huggingface-nlp/datasets/wit/"
_URLS = {
'train': [_URL + f"part-{'%05d' % i}-48a6f07e-bb86-4735-aac7-883349f41a28-c000.json.gz" for i in range(400)]
}
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'),
'original_height': Value('int32'),
'original_width': Value('int32'),
'mime_type': Value('string'),
'caption_attribution_description': Value('string'),
'wit_features': Sequence(
{
"language": Value('string'),
"page_url": Value('string'),
"attribution_passes_lang_id": Value("string"),
"caption_alt_text_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'),
"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']:
# If a feature is missing from feature dict, add it as None
for fname in wit_feature_names:
if fname not in feature:
feature[fname] = None
# Here we take redundant values from wit_features and add them to row_data to avoid unnecessary duplication
extra_wit_feature_keys = [k for k in feature.keys() if k not in wit_feature_names]
for k in extra_wit_feature_keys:
data = feature.pop(k)
if isinstance(data, list):
data = data[0]
row_data[k] = data
# Check row_data now for missing keys, adding None for most, but -1 for int features to avoid failures.
missing_keys = [x for x in self.info.features.keys() if x not in row_data]
for missing_key in missing_keys:
row_data[missing_key] = None if missing_key not in ['original_height', 'original_width'] else -1
yield str(i), row_data
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