Datasets:
hlgd

	# 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.
	"""
	HLGD is a binary classification dataset consisting of 20,056 labeled news headlines pairs indicating
	whether the two headlines describe the same underlying world event or not.
	"""

	import json
	import os

	import datasets


	_CITATION = """\
	@inproceedings{Laban2021NewsHG,
	title={News Headline Grouping as a Challenging NLU Task},
	author={Philippe Laban and Lucas Bandarkar},
	booktitle={NAACL 2021},
	publisher = {Association for Computational Linguistics},
	year={2021}
	}
	"""

	_DESCRIPTION = """\
	HLGD is a binary classification dataset consisting of 20,056 labeled news headlines pairs indicating
	whether the two headlines describe the same underlying world event or not.
	"""

	_HOMEPAGE = "https://github.com/tingofurro/headline_grouping"
	_LICENSE = "Apache-2.0 License"
	_DOWNLOAD_URL = "https://github.com/tingofurro/headline_grouping/releases/download/0.1/hlgd_classification_0.1.zip"


	class HLGD(datasets.GeneratorBasedBuilder):
	"""Headline Grouping Dataset."""

	VERSION = datasets.Version("1.1.0")

	def _info(self):
	features = datasets.Features(
	{
	"timeline_id": datasets.features.ClassLabel(names=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
	"headline_a": datasets.Value("string"),
	"headline_b": datasets.Value("string"),
	"date_a": datasets.Value("string"),
	"date_b": datasets.Value("string"),
	"url_a": datasets.Value("string"),
	"url_b": datasets.Value("string"),
	"label": datasets.features.ClassLabel(names=["same_event", "different_event"]),
	}
	)

	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
	# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

	# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
	# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
	# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive

	data_dir = dl_manager.download_and_extract(_DOWNLOAD_URL)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepath": os.path.join(data_dir, "train.json"),
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={"filepath": os.path.join(data_dir, "test.json"), "split": "test"},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"filepath": os.path.join(data_dir, "dev.json"),
	"split": "dev",
	},
	),
	]

	def _generate_examples(
	self, filepath, split # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	):
	"""Yields examples as (key, example) tuples."""
	# This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
	# The `key` is here for legacy reason (tfds) and is not important in itself.

	with open(filepath, encoding="utf-8") as f:
	dataset_split = json.load(f)

	for id_, row in enumerate(dataset_split):
	yield id_, {
	"timeline_id": row["timeline_id"],
	"headline_a": row["headline_a"],
	"headline_b": row["headline_b"],
	"date_a": row["date_a"],
	"date_b": row["date_b"],
	"url_a": row["url_a"],
	"url_b": row["url_b"],
	"label": row["label"],
	}