Datasets:
cc100

	# coding=utf-8
	# Copyright 2020 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
	import datasets


	_DESCRIPTION = """\
	This corpus is an attempt to recreate the dataset used for training XLM-R. This corpus comprises of monolingual data for 100+ languages and also includes data for romanized languages (indicated by *_rom). This was constructed using the urls and paragraph indices provided by the CC-Net repository by processing January-December 2018 Commoncrawl snapshots. Each file comprises of documents separated by double-newlines and paragraphs within the same document separated by a newline. The data is generated using the open source CC-Net repository. No claims of intellectual property are made on the work of preparation of the corpus.
	"""
	_HOMEPAGE_URL = "https://data.statmt.org/cc-100/"
	_CITATION = """\
	@inproceedings{conneau-etal-2020-unsupervised,
	title = "Unsupervised Cross-lingual Representation Learning at Scale",
	author = "Conneau, Alexis and
	Khandelwal, Kartikay and
	Goyal, Naman and
	Chaudhary, Vishrav and
	Wenzek, Guillaume and
	Guzm{\\'a}n, Francisco and
	Grave, Edouard and
	Ott, Myle and
	Zettlemoyer, Luke and
	Stoyanov, Veselin",
	editor = "Jurafsky, Dan and
	Chai, Joyce and
	Schluter, Natalie and
	Tetreault, Joel",
	booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
	month = jul,
	year = "2020",
	address = "Online",
	publisher = "Association for Computational Linguistics",
	url = "https://aclanthology.org/2020.acl-main.747",
	doi = "10.18653/v1/2020.acl-main.747",
	pages = "8440--8451",
	}
	@inproceedings{wenzek-etal-2020-ccnet,
	title = "{CCN}et: Extracting High Quality Monolingual Datasets from Web Crawl Data",
	author = "Wenzek, Guillaume and
	Lachaux, Marie-Anne and
	Conneau, Alexis and
	Chaudhary, Vishrav and
	Guzm{\\'a}n, Francisco and
	Joulin, Armand and
	Grave, Edouard",
	editor = "Calzolari, Nicoletta and
	B{\\'e}chet, Fr{\\'e}d{\\'e}ric and
	Blache, Philippe and
	Choukri, Khalid and
	Cieri, Christopher and
	Declerck, Thierry and
	Goggi, Sara and
	Isahara, Hitoshi and
	Maegaard, Bente and
	Mariani, Joseph and
	Mazo, H{\\'e}l{\\`e}ne and
	Moreno, Asuncion and
	Odijk, Jan and
	Piperidis, Stelios",
	booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
	month = may,
	year = "2020",
	address = "Marseille, France",
	publisher = "European Language Resources Association",
	url = "https://aclanthology.org/2020.lrec-1.494",
	pages = "4003--4012",
	language = "English",
	ISBN = "979-10-95546-34-4",
	}
	"""

	_VERSION = "1.0.0"
	_BASE_URL = "https://data.statmt.org/cc-100/{}.txt.xz"
	_LANGUAGES = [
	"af",
	"am",
	"ar",
	"as",
	"az",
	"be",
	"bg",
	"bn",
	"bn_rom",
	"br",
	"bs",
	"ca",
	"cs",
	"cy",
	"da",
	"de",
	"el",
	"en",
	"eo",
	"es",
	"et",
	"eu",
	"fa",
	"ff",
	"fi",
	"fr",
	"fy",
	"ga",
	"gd",
	"gl",
	"gn",
	"gu",
	"ha",
	"he",
	"hi",
	"hi_rom",
	"hr",
	"ht",
	"hu",
	"hy",
	"id",
	"ig",
	"is",
	"it",
	"ja",
	"jv",
	"ka",
	"kk",
	"km",
	"kn",
	"ko",
	"ku",
	"ky",
	"la",
	"lg",
	"li",
	"ln",
	"lo",
	"lt",
	"lv",
	"mg",
	"mk",
	"ml",
	"mn",
	"mr",
	"ms",
	"my",
	"my_zaw",
	"ne",
	"nl",
	"no",
	"ns",
	"om",
	"or",
	"pa",
	"pl",
	"ps",
	"pt",
	"qu",
	"rm",
	"ro",
	"ru",
	"sa",
	"sc",
	"sd",
	"si",
	"sk",
	"sl",
	"so",
	"sq",
	"sr",
	"ss",
	"su",
	"sv",
	"sw",
	"ta",
	"ta_rom",
	"te",
	"te_rom",
	"th",
	"tl",
	"tn",
	"tr",
	"ug",
	"uk",
	"ur",
	"ur_rom",
	"uz",
	"vi",
	"wo",
	"xh",
	"yi",
	"yo",
	"zh-Hans",
	"zh-Hant",
	"zu",
	]


	class Cc100Config(datasets.BuilderConfig):
	def __init__(self, args, lang=None, *kwargs):
	super().__init__(
	*args,
	name=f"{lang}",
	**kwargs,
	)
	self.lang = lang


	class Cc100(datasets.GeneratorBasedBuilder):
	BUILDER_CONFIGS = [
	Cc100Config(
	lang=lang,
	description=f"Language: {lang}",
	version=datasets.Version(_VERSION),
	)
	for lang in _LANGUAGES
	]
	BUILDER_CONFIG_CLASS = Cc100Config

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

	def _split_generators(self, dl_manager):
	def _base_url(lang):
	return _BASE_URL.format(lang)

	download_url = _base_url(self.config.lang)
	path = dl_manager.download_and_extract(download_url)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"datapath": path},
	)
	]

	def _generate_examples(self, datapath):
	with open(datapath, encoding="utf-8") as f:
	for sentence_counter, row in enumerate(f):
	result = (
	sentence_counter,
	{
	"id": str(sentence_counter),
	"text": row,
	},
	)
	yield result