Datasets:
Tasks:
Text Classification
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
expert-generated
Annotations Creators:
crowdsourced
Source Datasets:
original
Tags:
headline-grouping
License:
# 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"], | |
} | |