Datasets:
twi_text_c3

	# 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
	"""The BookCorpus dataset."""


	import datasets


	_DESCRIPTION = """\
	Twi Text C3 is the largest Twi texts collected and used to train FastText embeddings in the
	YorubaTwi Embedding paper: https://www.aclweb.org/anthology/2020.lrec-1.335/
	"""

	_CITATION = """\
	@inproceedings{alabi-etal-2020-massive,
	title = "Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of Yoruba and {T}wi",
	author = "Alabi, Jesujoba and
	Amponsah-Kaakyire, Kwabena and
	Adelani, David and
	Espa{\\~n}a-Bonet, Cristina",
	booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
	month = may,
	year = "2020",
	address = "Marseille, France",
	publisher = "European Language Resources Association",
	url = "https://www.aclweb.org/anthology/2020.lrec-1.335",
	pages = "2754--2762",
	language = "English",
	ISBN = "979-10-95546-34-4",
	}
	"""

	URL = "https://drive.google.com/uc?export=download&id=1s8NSFT4Kz0caKZ4VybPNzt88F8ZanprY"


	class TwiTextC3Config(datasets.BuilderConfig):
	"""BuilderConfig for Twi Text C3."""

	def __init__(self, **kwargs):
	"""BuilderConfig for BookCorpus.
	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(TwiTextC3Config, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)


	class TwiTextC3(datasets.GeneratorBasedBuilder):
	"""Twi Text C3 dataset."""

	BUILDER_CONFIGS = [
	TwiTextC3Config(
	name="plain_text",
	description="Plain text",
	)
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"text": datasets.Value("string"),
	}
	),
	supervised_keys=None,
	homepage="https://www.aclweb.org/anthology/2020.lrec-1.335/",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	arch_path = dl_manager.download_and_extract(URL)

	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": arch_path}),
	]

	def _generate_examples(self, filepath):
	with open(filepath, mode="r", encoding="utf-8") as f:
	lines = f.read().splitlines()
	for id, line in enumerate(lines):
	yield id, {"text": line.strip()}