ans-stance / ans-stance.py
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# 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