Datasets:
Tasks:
Text Classification
Sub-tasks:
fact-checking
Languages:
Arabic
Size:
1K<n<10K
ArXiv:
Tags:
stance-detection
License:
# Copyright 2022 Mads Kongsbak and Leon Derczynski | |
# | |
# 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. | |
"""DataLoader for ANS, an Arabic News Stance corpus""" | |
import csv | |
import json | |
import os | |
import datasets | |
_CITATION = """\ | |
@inproceedings{, | |
title = "Stance Prediction and Claim Verification: An {A}rabic Perspective", | |
author = "Khouja, Jude", | |
booktitle = "Proceedings of the Third Workshop on Fact Extraction and {VER}ification ({FEVER})", | |
year = "2020", | |
address = "Seattle, USA", | |
publisher = "Association for Computational Linguistics", | |
} | |
""" | |
_DESCRIPTION = """\ | |
The dataset is a collection of news titles in arabic along with paraphrased and corrupted titles. The stance prediction version is a 3-class classification task. Data contains three columns: s1, s2, stance. | |
""" | |
_HOMEPAGE = "" | |
_LICENSE = "apache-2.0" | |
class ANSStanceConfig(datasets.BuilderConfig): | |
def __init__(self, **kwargs): | |
super(ANSStanceConfig, self).__init__(**kwargs) | |
class ANSStance(datasets.GeneratorBasedBuilder): | |
"""ANS dataset made in triples of (s1, s2, stance)""" | |
VERSION = datasets.Version("1.0.0") | |
BUILDER_CONFIGS = [ | |
ANSStanceConfig(name="stance", version=VERSION, description=""), | |
] | |
def _info(self): | |
features = datasets.Features( | |
{ | |
"id": datasets.Value("string"), | |
"s1": datasets.Value("string"), | |
"s2": datasets.Value("string"), | |
"stance": datasets.features.ClassLabel( | |
names=[ | |
"disagree", | |
"agree", | |
"other" | |
] | |
) | |
} | |
) | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=features, | |
homepage=_HOMEPAGE, | |
license=_LICENSE, | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
train_text = dl_manager.download_and_extract("ans_train.csv") | |
valid_text = dl_manager.download_and_extract("ans_dev.csv") | |
test_text = dl_manager.download_and_extract("ans_test.csv") | |
return [ | |
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_text, "split": "train"}), | |
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": valid_text, "split": "validation"}), | |
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_text, "split": "test"}), | |
] | |
def _generate_examples(self, filepath, split): | |
with open(filepath, encoding="utf-8") as f: | |
reader = csv.DictReader(f, delimiter=",") | |
guid = 0 | |
for instance in reader: | |
instance["s1"] = instance.pop("s1") | |
instance["s2"] = instance.pop("s2") | |
instance["stance"] = instance.pop("stance") | |
instance['id'] = str(guid) | |
yield guid, instance | |
guid += 1 |