Datasets:
newsph_nli

	# 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.
	"""NewsPH-NLI Sentence Entailment Dataset in Filipino"""

	import csv
	import os

	import datasets


	_DESCRIPTION = """\
	First benchmark dataset for sentence entailment in the low-resource Filipino language. Constructed through exploting the structure of news articles. Contains 600,000 premise-hypothesis pairs, in 70-15-15 split for training, validation, and testing.
	"""

	_CITATION = """\
	@article{cruz2020investigating,
	title={Investigating the True Performance of Transformers in Low-Resource Languages: A Case Study in Automatic Corpus Creation},
	author={Jan Christian Blaise Cruz and Jose Kristian Resabal and James Lin and Dan John Velasco and Charibeth Cheng},
	journal={arXiv preprint arXiv:2010.11574},
	year={2020}
	}
	"""

	_HOMEPAGE = "https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks"

	# TODO: Add the licence for the dataset here if you can find it
	_LICENSE = ""

	_URL = "https://s3.us-east-2.amazonaws.com/blaisecruz.com/datasets/newsph/newsph-nli.zip"


	class NewsphNli(datasets.GeneratorBasedBuilder):
	"""NewsPH-NLI Sentence Entailment Dataset in Filipino"""

	VERSION = datasets.Version("1.0.0")

	def _info(self):
	features = datasets.Features(
	{
	"premise": datasets.Value("string"),
	"hypothesis": datasets.Value("string"),
	"label": datasets.features.ClassLabel(names=["0", "1"]),
	}
	)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	data_dir = dl_manager.download_and_extract(_URL)
	download_path = os.path.join(data_dir, "newsph-nli")
	train_path = os.path.join(download_path, "train.csv")
	test_path = os.path.join(download_path, "train.csv")
	validation_path = os.path.join(download_path, "valid.csv")

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepath": train_path,
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"filepath": test_path,
	"split": "test",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"filepath": validation_path,
	"split": "dev",
	},
	),
	]

	def _generate_examples(self, filepath, split):
	""" Yields examples. """
	with open(filepath, encoding="utf-8") as csv_file:
	csv_reader = csv.reader(
	csv_file, quotechar='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True
	)
	next(csv_reader)
	for id_, row in enumerate(csv_reader):
	premise, hypothesis, label = row
	yield id_, {"premise": premise, "hypothesis": hypothesis, "label": label}