Datasets:
large_spanish_corpus

	# 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.
	"""The Large Spanish Corpus is a compilation of Spanish corpora spanning Wikipedia to European parliament notes."""


	import os

	import datasets


	_CITATION = """\
	@dataset{jose_canete_2019_3247731,
	author = {José Cañete},
	title = {Compilation of Large Spanish Unannotated Corpora},
	month = may,
	year = 2019,
	publisher = {Zenodo},
	doi = {10.5281/zenodo.3247731},
	url = {https://doi.org/10.5281/zenodo.3247731}
	}
	"""

	_DESCRIPTION = """\
	The Large Spanish Corpus is a compilation of 15 unlabelled Spanish corpora spanning Wikipedia to European parliament \
	notes. Each config contains the data corresponding to a different corpus. For example, "all_wiki" only includes \
	examples from Spanish Wikipedia. By default, the config is set to "combined" which loads all the corpora; with this \
	setting you can also specify the number of samples to return per corpus by configuring the "split" argument.
	"""

	_HOMEPAGE = "https://github.com/josecannete/spanish-corpora"

	_LICENSE = "MIT"

	_URL = "data/raw.zip"

	_CORPORA = [
	"JRC",
	"EMEA",
	"GlobalVoices",
	"ECB",
	"DOGC",
	"all_wikis",
	"TED",
	"multiUN",
	"Europarl",
	"NewsCommentary11",
	"UN",
	"EUBookShop",
	"ParaCrawl",
	"OpenSubtitles2018",
	"DGT",
	]

	_CORPORA_FILEPATHS = {corpus: os.path.join("spanish-corpora", "raw", f"{corpus}.txt") for corpus in _CORPORA}

	_VERSION = "1.1.0"

	_COMBINED = "combined"


	class LargeSpanishCorpusConfig(datasets.BuilderConfig):
	def __init__(self, corpora=None, **kwargs):
	super(LargeSpanishCorpusConfig, self).__init__(version=datasets.Version(_VERSION, ""), **kwargs)
	self.corpora = corpora

	@property
	def filepaths(self):
	return [_CORPORA_FILEPATHS[corpus] for corpus in self.corpora]


	class LargeSpanishCorpus(datasets.GeneratorBasedBuilder):
	"""The Large Spanish Corpus."""

	BUILDER_CONFIGS = [
	LargeSpanishCorpusConfig(name=corpus, corpora=[corpus], description=f"Spanish examples in corpus {corpus}.")
	for corpus in _CORPORA
	] + [
	LargeSpanishCorpusConfig(
	name=_COMBINED, corpora=_CORPORA, description="Complete Spanish dataset with all corpora."
	)
	]
	BUILDER_CONFIG_CLASS = LargeSpanishCorpusConfig
	DEFAULT_CONFIG_NAME = _COMBINED

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

	def _split_generators(self, dl_manager):
	data_dir = dl_manager.download_and_extract(_URL)
	return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"data_dir": data_dir})]

	def _generate_examples(self, data_dir):
	_id = 0
	for filepath in self.config.filepaths:
	filepath = os.path.join(data_dir, filepath)
	with open(filepath, mode="r", encoding="utf-8") as f:
	for line in f:
	yield _id, {"text": line.strip()},
	_id += 1