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# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# 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.
# Lint as: python3
"""Arabic Wiki Question Answering corpus."""
import csv
import datasets
_CITATION = """\
@InProceedings{YangYihMeek:EMNLP2015:WikiQA,
author = {{Yi}, Yang and {Wen-tau}, Yih and {Christopher} Meek},
title = "{WikiQA: A Challenge Dataset for Open-Domain Question Answering}",
journal = {Association for Computational Linguistics},
year = 2015,
doi = {10.18653/v1/D15-1237},
pages = {2013–2018},
}
"""
_DESCRIPTION = """\
Arabic Version of WikiQA by automatic automatic machine translators \
and crowdsourced the selection of the best one to be incorporated into the corpus
"""
_URL = "https://raw.githubusercontent.com/qcri/WikiQAar/master/"
_URL_FILES = {
"train": _URL + "WikiQAar-train.tsv",
"dev": _URL + "WikiQAar-dev.tsv",
"test": _URL + "WikiQAar-test.tsv",
}
class WikiQaArConfig(datasets.BuilderConfig):
"""BuilderConfig for WikiQaAr."""
def __init__(self, **kwargs):
"""BuilderConfig for WikiQaAr.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(WikiQaArConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
class WikiQaAr(datasets.GeneratorBasedBuilder):
"""WikiQaAr dataset."""
BUILDER_CONFIGS = [
WikiQaArConfig(
name="plain_text",
description="Plain text",
)
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"question_id": datasets.Value("string"),
"question": datasets.Value("string"),
"document_id": datasets.Value("string"),
"answer_id": datasets.Value("string"),
"answer": datasets.Value("string"),
"label": datasets.features.ClassLabel(num_classes=2),
}
),
supervised_keys=None,
homepage="https://github.com/qcri/WikiQAar",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
dl_dir = dl_manager.download_and_extract(_URL_FILES)
return [
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": dl_dir["test"]}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": dl_dir["dev"]}),
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": dl_dir["train"]}),
]
def _generate_examples(self, filepath):
"""Yields examples."""
with open(filepath, encoding="utf-8") as f:
reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
for _id, row in enumerate(reader):
# ignore some entries with errors
if len(row) > 6 or len(row["Label"]) == 0:
continue
yield str(_id), {
"question_id": row["QuestionID"],
"question": row["Question"],
"document_id": row["DocumentID"],
"answer_id": row["SentenceID"],
"answer": row["Sentence"],
"label": row["Label"],
}
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