Datasets:

wikimedia
/

wit_base

	# 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.
	""""WIT (Wikipedia-based Image Text Dataset) dataset (Wikimedia version)."""

	import base64
	import gzip
	import json

	import datasets

	from .corrected_examples import CORRECTED_EXAMPLES


	_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.
	It contains more than six million images from Wikipedia articles in 100+ languages, which correspond to almost all captioned images in Google's version of the WIT dataset.
	Images are provided at a 300-px resolution, a size that is suitable for most of the learning frameworks used to classify and analyze images.
	This version of the WIT dataset was released by Wikimedia Research team.
	"""

	_LICENSE = "CC BY-SA 4.0 international license"

	_HOMEPAGE = "https://techblog.wikimedia.org/2021/09/09/the-wikipedia-image-caption-matching-challenge-and-a-huge-release-of-image-data-for-research/"

	_BASE_URL = "https://storage.googleapis.com/huggingface-nlp/datasets/wit/"

	_URLS = [_BASE_URL + f"part-{'%05d' % i}-48a6f07e-bb86-4735-aac7-883349f41a28-c000.json.gz" for i in range(400)]


	class Wit(datasets.GeneratorBasedBuilder):
	"""Builder for WIT dataset (Wikimedia version)."""

	DEFAULT_WRITER_BATCH_SIZE = 1000

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"image": datasets.Image(),
	"image_url": datasets.Value("string"),
	"embedding": datasets.Sequence(datasets.Value("float64"), length=2048),
	"metadata_url": datasets.Value("string"),
	"original_height": datasets.Value("int32"),
	"original_width": datasets.Value("int32"),
	"mime_type": datasets.Value("string"),
	"caption_attribution_description": datasets.Value("string"),
	"wit_features": datasets.Sequence(
	{
	"language": datasets.Value("string"),
	"page_url": datasets.Value("string"),
	"attribution_passes_lang_id": datasets.Value("bool"),
	"caption_alt_text_description": datasets.Value("string"),
	"caption_reference_description": datasets.Value("string"),
	"caption_title_and_reference_description": datasets.Value("string"),
	"context_page_description": datasets.Value("string"),
	"context_section_description": datasets.Value("string"),
	"hierarchical_section_title": datasets.Value("string"),
	"is_main_image": datasets.Value("bool"),
	"page_changed_recently": datasets.Value("bool"),
	"page_title": datasets.Value("string"),
	"section_title": datasets.Value("string"),
	}
	),
	}
	),
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	downloaded_files = dl_manager.download(_URLS)
	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"data_files": downloaded_files}),
	]

	def _generate_examples(self, data_files):
	"""Yields examples."""
	wit_feature_names = self.info.features["wit_features"].feature.keys()
	idx = 0
	for data_file_idx, data_file in enumerate(data_files):
	with gzip.open(open(data_file, "rb"), mode="rt", encoding="utf-8") as f:
	for row_idx, row in enumerate(f):
	example = json.loads(row)
	ex_wit_features_non_empty = []
	for feature in example["wit_features"]:
	# If a feature is missing from feature dict, add it as None
	for wit_feature_name in wit_feature_names:
	if wit_feature_name not in feature:
	feature[wit_feature_name] = None
	# Here we take redundant values from wit_features and add them to example to avoid unnecessary duplication
	extra_wit_feature_keys = [k for k in feature.keys() if k not in wit_feature_names]
	for extra_wit_feature_key in extra_wit_feature_keys:
	extra_wit_feature_value = feature.pop(extra_wit_feature_key)
	if isinstance(extra_wit_feature_value, list):
	extra_wit_feature_value = extra_wit_feature_value[0]
	example[extra_wit_feature_key] = extra_wit_feature_value
	# Remove empty wit features
	if any(v is not None for v in feature.values()):
	ex_wit_features_non_empty.append(feature)
	example["wit_features"] = ex_wit_features_non_empty
	# Check example now for missing keys, adding None to avoid failures
	missing_keys = [k for k in self.info.features.keys() if k not in example]
	for missing_key in missing_keys:
	example[missing_key] = None
	# Decode base64 encoded image bytes
	b64_image_bytes = example.pop("b64_bytes")
	example["image"] = (
	{"path": None, "bytes": base64.b64decode(b64_image_bytes)}
	if b64_image_bytes is not None
	else None
	)
	corrections = CORRECTED_EXAMPLES.get((data_file_idx, row_idx))
	if corrections is not None:
	assert example["metadata_url"] == corrections["metadata_url"]
	example.update(corrections)
	yield idx, example
	idx += 1