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# 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"],
}
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