Datasets:
wili_2018

	# 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.
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	#
	# http://www.apache.org/licenses/LICENSE-2.0
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	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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	# limitations under the License.

	"""WiLI-2018, the Wikipedia language identification benchmark dataset"""


	import datasets
	from datasets.tasks import TextClassification


	_CITATION = """\
	@dataset{thoma_martin_2018_841984,
	author = {Thoma, Martin},
	title = {{WiLI-2018 - Wikipedia Language Identification database}},
	month = jan,
	year = 2018,
	publisher = {Zenodo},
	version = {1.0.0},
	doi = {10.5281/zenodo.841984},
	url = {https://doi.org/10.5281/zenodo.841984}
	}
	"""

	_DESCRIPTION = """\
	It is a benchmark dataset for language identification and contains 235000 paragraphs of 235 languages
	"""

	# TODO: Add a link to an official homepage for the dataset here
	_HOMEPAGE = "https://zenodo.org/record/841984"

	# TODO: Add the licence for the dataset here if you can find it
	_LICENSE = "ODC Open Database License v1.0"


	# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
	_TRAIN_DOWNLOAD_URL = "https://drive.google.com/uc?export=download&id=1ZzlIQvw1KNBG97QQCfdatvVrrbeLaM1u"
	_TEST_DOWNLOAD_URL = "https://drive.google.com/uc?export=download&id=1Xx4kFc1Xdzz8AhDasxZ0cSa-a35EQSDZ"

	_CLASSES = [
	"cdo",
	"glk",
	"jam",
	"lug",
	"san",
	"rue",
	"wol",
	"new",
	"mwl",
	"bre",
	"ara",
	"hye",
	"xmf",
	"ext",
	"cor",
	"yor",
	"div",
	"asm",
	"lat",
	"cym",
	"hif",
	"ace",
	"kbd",
	"tgk",
	"rus",
	"nso",
	"mya",
	"msa",
	"ava",
	"cbk",
	"urd",
	"deu",
	"swa",
	"pus",
	"bxr",
	"udm",
	"csb",
	"yid",
	"vro",
	"por",
	"pdc",
	"eng",
	"tha",
	"hat",
	"lmo",
	"pag",
	"jav",
	"chv",
	"nan",
	"sco",
	"kat",
	"bho",
	"bos",
	"kok",
	"oss",
	"mri",
	"fry",
	"cat",
	"azb",
	"kin",
	"hin",
	"sna",
	"dan",
	"egl",
	"mkd",
	"ron",
	"bul",
	"hrv",
	"som",
	"pam",
	"nav",
	"ksh",
	"nci",
	"khm",
	"sgs",
	"srn",
	"bar",
	"cos",
	"ckb",
	"pfl",
	"arz",
	"roa-tara",
	"fra",
	"mai",
	"zh-yue",
	"guj",
	"fin",
	"kir",
	"vol",
	"hau",
	"afr",
	"uig",
	"lao",
	"swe",
	"slv",
	"kor",
	"szl",
	"srp",
	"dty",
	"nrm",
	"dsb",
	"ind",
	"wln",
	"pnb",
	"ukr",
	"bpy",
	"vie",
	"tur",
	"aym",
	"lit",
	"zea",
	"pol",
	"est",
	"scn",
	"vls",
	"stq",
	"gag",
	"grn",
	"kaz",
	"ben",
	"pcd",
	"bjn",
	"krc",
	"amh",
	"diq",
	"ltz",
	"ita",
	"kab",
	"bel",
	"ang",
	"mhr",
	"che",
	"koi",
	"glv",
	"ido",
	"fao",
	"bak",
	"isl",
	"bcl",
	"tet",
	"jpn",
	"kur",
	"map-bms",
	"tyv",
	"olo",
	"arg",
	"ori",
	"lim",
	"tel",
	"lin",
	"roh",
	"sqi",
	"xho",
	"mlg",
	"fas",
	"hbs",
	"tam",
	"aze",
	"lad",
	"nob",
	"sin",
	"gla",
	"nap",
	"snd",
	"ast",
	"mal",
	"mdf",
	"tsn",
	"nds",
	"tgl",
	"nno",
	"sun",
	"lzh",
	"jbo",
	"crh",
	"pap",
	"oci",
	"hak",
	"uzb",
	"zho",
	"hsb",
	"sme",
	"mlt",
	"vep",
	"lez",
	"nld",
	"nds-nl",
	"mrj",
	"spa",
	"ceb",
	"ina",
	"heb",
	"hun",
	"que",
	"kaa",
	"mar",
	"vec",
	"frp",
	"ell",
	"sah",
	"eus",
	"ces",
	"slk",
	"chr",
	"lij",
	"nep",
	"srd",
	"ilo",
	"be-tarask",
	"bod",
	"orm",
	"war",
	"glg",
	"mon",
	"gle",
	"min",
	"ibo",
	"ile",
	"epo",
	"lav",
	"lrc",
	"als",
	"mzn",
	"rup",
	"fur",
	"tat",
	"myv",
	"pan",
	"ton",
	"kom",
	"wuu",
	"tcy",
	"tuk",
	"kan",
	"ltg",
	]


	class Wili_2018(datasets.GeneratorBasedBuilder):
	"""WiLI Language Identification Dataset"""

	VERSION = datasets.Version("1.1.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="WiLI-2018 dataset",
	version=VERSION,
	description="Plain text of import of WiLI-2018",
	)
	]

	def _info(self):

	return datasets.DatasetInfo(
	# This is the description that will appear on the datasets page.
	description=_DESCRIPTION,
	# This defines the different columns of the dataset and their types
	features=datasets.Features(
	{"sentence": datasets.Value("string"), "label": datasets.features.ClassLabel(names=_CLASSES)}
	),
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	task_templates=[TextClassification(text_column="sentence", label_column="label")],
	)

	def _split_generators(self, dl_manager):
	train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
	test_path = dl_manager.download_and_extract(_TEST_DOWNLOAD_URL)
	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
	datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
	]

	def _generate_examples(self, filepath):

	with open(filepath, encoding="utf-8") as f:
	for id_, line in enumerate(f):
	text, label = line.rsplit(",", 1)
	text = text.strip('"')
	label = int(label.strip())
	yield id_, {"sentence": text, "label": label - 1}