Datasets:

kmi-linguistics
/

ilist

	# 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.
	"""Indo-Aryan Language Identification Shared Task Dataset"""

	from __future__ import absolute_import, division, print_function

	import datasets


	_CITATION = """\
	@proceedings{ws-2018-nlp-similar,
	title = "Proceedings of the Fifth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial 2018)",
	editor = {Zampieri, Marcos and
	Nakov, Preslav and
	Ljube{\v{s}}i{\'c}, Nikola and
	Tiedemann, J{\"o}rg and
	Malmasi, Shervin and
	Ali, Ahmed},
	month = aug,
	year = "2018",
	address = "Santa Fe, New Mexico, USA",
	publisher = "Association for Computational Linguistics",
	url = "https://www.aclweb.org/anthology/W18-3900",
	}
	"""

	_DESCRIPTION = """\
	This dataset is introduced in a task which aimed at identifying 5 closely-related languages of Indo-Aryan language family –
	Hindi (also known as Khari Boli), Braj Bhasha, Awadhi, Bhojpuri, and Magahi.
	"""

	_URL = "https://raw.githubusercontent.com/kmi-linguistics/vardial2018/master/dataset/{}.txt"


	class Ilist(datasets.GeneratorBasedBuilder):
	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"language_id": datasets.ClassLabel(names=["AWA", "BRA", "MAG", "BHO", "HIN"]),
	"text": datasets.Value("string"),
	}
	),
	supervised_keys=None,
	homepage="https://github.com/kmi-linguistics/vardial2018",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	filepaths = dl_manager.download_and_extract(
	{
	"train": _URL.format("train"),
	"test": _URL.format("gold"),
	"dev": _URL.format("dev"),
	}
	)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": filepaths["train"],
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": filepaths["test"],
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": filepaths["dev"],
	},
	),
	]

	def _generate_examples(self, filepath):
	""" Yields examples. """
	with open(filepath, "r", encoding="utf-8") as file:
	for idx, row in enumerate(file):
	row = row.strip("\n").split("\t")
	if len(row) == 1:
	continue
	yield idx, {"language_id": row[1], "text": row[0]}