Datasets:

google
/

wiki40b

	# coding=utf-8
	# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
	#
	# 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.

	"""Wiki40B: A clean Wikipedia dataset for 40+ languages."""


	import datasets


	logger = datasets.logging.get_logger(__name__)


	_CITATION = """
	"""

	_DESCRIPTION = """
	Clean-up text for 40+ Wikipedia languages editions of pages
	correspond to entities. The datasets have train/dev/test splits per language.
	The dataset is cleaned up by page filtering to remove disambiguation pages,
	redirect pages, deleted pages, and non-entity pages. Each example contains the
	wikidata id of the entity, and the full Wikipedia article after page processing
	that removes non-content sections and structured objects.
	"""

	_LICENSE = """
	This work is licensed under the Creative Commons Attribution-ShareAlike
	3.0 Unported License. To view a copy of this license, visit
	http://creativecommons.org/licenses/by-sa/3.0/ or send a letter to
	Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
	"""

	_URL = "https://research.google/pubs/pub49029/"

	_DATA_DIRECTORY = "gs://tfds-data/downloads/wiki40b/tfrecord_prod"

	WIKIPEDIA_LANGUAGES = [
	"en",
	"ar",
	"zh-cn",
	"zh-tw",
	"nl",
	"fr",
	"de",
	"it",
	"ja",
	"ko",
	"pl",
	"pt",
	"ru",
	"es",
	"th",
	"tr",
	"bg",
	"ca",
	"cs",
	"da",
	"el",
	"et",
	"fa",
	"fi",
	"he",
	"hi",
	"hr",
	"hu",
	"id",
	"lt",
	"lv",
	"ms",
	"no",
	"ro",
	"sk",
	"sl",
	"sr",
	"sv",
	"tl",
	"uk",
	"vi",
	]


	class Wiki40bConfig(datasets.BuilderConfig):
	"""BuilderConfig for Wiki40B."""

	def __init__(self, language=None, **kwargs):
	"""BuilderConfig for Wiki40B.

	Args:
	language: string, the language code for the Wiki40B dataset to use.
	**kwargs: keyword arguments forwarded to super.
	"""
	super(Wiki40bConfig, self).__init__(
	name=str(language), description=f"Wiki40B dataset for {language}.", **kwargs
	)
	self.language = language


	_VERSION = datasets.Version("1.1.0")


	class Wiki40b(datasets.BeamBasedBuilder):
	"""Wiki40B: A Clean Wikipedia Dataset for Mutlilingual Language Modeling."""

	BUILDER_CONFIGS = [
	Wiki40bConfig(
	version=_VERSION,
	language=lang,
	) # pylint:disable=g-complex-comprehension
	for lang in WIKIPEDIA_LANGUAGES
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"wikidata_id": datasets.Value("string"),
	"text": datasets.Value("string"),
	"version_id": datasets.Value("string"),
	}
	),
	supervised_keys=None,
	homepage=_URL,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""

	lang = self.config.language

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"filepaths": f"{_DATA_DIRECTORY}/train/{lang}_examples-*"},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={"filepaths": f"{_DATA_DIRECTORY}/dev/{lang}_examples-*"},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={"filepaths": f"{_DATA_DIRECTORY}/test/{lang}_examples-*"},
	),
	]

	def _build_pcollection(self, pipeline, filepaths):
	"""Build PCollection of examples."""
	import apache_beam as beam
	import tensorflow as tf

	logger.info("generating examples from = %s", filepaths)

	def _extract_content(example):
	"""Extracts content from a TFExample."""
	wikidata_id = example.features.feature["wikidata_id"].bytes_list.value[0].decode("utf-8")
	text = example.features.feature["text"].bytes_list.value[0].decode("utf-8")
	version_id = example.features.feature["version_id"].bytes_list.value[0].decode("utf-8")

	# wikidata_id could be duplicated with different texts.
	yield wikidata_id + text, {
	"wikidata_id": wikidata_id,
	"text": text,
	"version_id": version_id,
	}

	return (
	pipeline
	\| beam.io.ReadFromTFRecord(filepaths, coder=beam.coders.ProtoCoder(tf.train.Example))
	\| beam.FlatMap(_extract_content)
	)