Datasets:

graelo
/

wikipedia

	# 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.

	# Lint as: python3
	"""Wikipedia dataset containing cleaned articles of all languages."""


	import json

	import datasets
	import pyarrow.parquet as pq

	from .lang_def import WIKIPEDIA_LANGUAGES

	logger = datasets.logging.get_logger(__name__)


	_CITATION = """\
	@ONLINE {wikidump,
	author = {Wikimedia Foundation},
	title = {Wikimedia Downloads},
	url = {https://dumps.wikimedia.org}
	}
	"""

	_DESCRIPTION = """\
	Wikipedia dataset containing cleaned articles of all languages.
	The datasets are built from the Wikipedia dump
	(https://dumps.wikimedia.org/) with one split per language. Each example
	contains the content of one full Wikipedia article with cleaning to strip
	markdown and unwanted sections (references, etc.).
	"""

	_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."
	)

	_BASE_URL_TMPL = "data/{date}/{lang}"

	_VERSION = datasets.Version("1.2.0", "")


	class WikipediaConfig(datasets.BuilderConfig):
	"""BuilderConfig for Wikipedia."""

	def __init__(self, language=None, date=None, version=_VERSION, **kwargs):
	"""BuilderConfig for Wikipedia.
	Args:
	language: string, the language code for the Wikipedia dump to use.
	date: string, date of the Wikipedia dump in YYYYMMDD format. A list of
	available dates can be found at https://dumps.wikimedia.org/enwiki/.
	**kwargs: keyword arguments forwarded to super.
	"""
	super().__init__(
	name=f"{date}.{language}",
	description=f"Wikipedia dataset for {language}, parsed from {date} dump.",
	version=version,
	**kwargs,
	)
	self.date = date
	self.language = language


	class Wikipedia(datasets.ArrowBasedBuilder):
	"""Wikipedia dataset."""

	# Use mirror (your.org) to avoid download caps.
	BUILDER_CONFIG_CLASS = WikipediaConfig
	BUILDER_CONFIGS = [
	WikipediaConfig(
	language=lang,
	date=date,
	) # pylint:disable=g-complex-comprehension
	for date in WIKIPEDIA_LANGUAGES.keys()
	for lang in WIKIPEDIA_LANGUAGES[date]
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"id": datasets.Value("string"),
	"url": datasets.Value("string"),
	"title": datasets.Value("string"),
	"text": datasets.Value("string"),
	}
	),
	# No default supervised_keys.
	supervised_keys=None,
	homepage="https://dumps.wikimedia.org",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	def _base_url(lang):
	return _BASE_URL_TMPL.format(lang=lang, date=self.config.date)

	lang = self.config.language

	info_file_url = _base_url(lang) + "/info.json"
	downloaded_info_file = dl_manager.download_and_extract({"info": info_file_url})
	with open(downloaded_info_file["info"], "r") as f:
	info = json.load(f)

	shard_urls = [
	_base_url(lang) + "/" + shard_name for shard_name in info["shards"]
	]
	downloaded_files = dl_manager.download({"train": shard_urls})
	logger.info("downloaded_files = %s", downloaded_files)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"filepaths": downloaded_files["train"]},
	),
	]

	def _generate_tables(self, filepaths):
	"""This function returns the examples in the raw (text) form."""

	for filepath in filepaths:
	with open(filepath, "rb") as f:
	pf = pq.ParquetFile(f)
	for group_i in range(pf.num_row_groups):
	tbl = pf.read_row_group(group_i)
	yield group_i, tbl