Datasets:
Tasks:
Question Answering
Formats:
parquet
Sub-tasks:
multiple-choice-qa
Languages:
English
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10K - 100K
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License:
File size: 5,796 Bytes
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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.
"""The COmmonsense Dataset Adversarially-authored by Humans (CODAH)"""
from __future__ import absolute_import, division, print_function
import csv
import datasets
_CITATION = """\
@inproceedings{chen2019codah,
title={CODAH: An Adversarially-Authored Question Answering Dataset for Common Sense},
author={Chen, Michael and D'Arcy, Mike and Liu, Alisa and Fernandez, Jared and Downey, Doug},
booktitle={Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for NLP},
pages={63--69},
year={2019}
}
"""
_DESCRIPTION = """\
The COmmonsense Dataset Adversarially-authored by Humans (CODAH) is an evaluation set for commonsense \
question-answering in the sentence completion style of SWAG. As opposed to other automatically \
generated NLI datasets, CODAH is adversarially constructed by humans who can view feedback \
from a pre-trained model and use this information to design challenging commonsense questions. \
Our experimental results show that CODAH questions present a complementary extension to the SWAG dataset, testing additional modes of common sense.
"""
_URL = "https://raw.githubusercontent.com/Websail-NU/CODAH/master/data/"
_FULL_DATA_URL = _URL + "full_data.tsv"
QUESTION_CATEGORIES_MAPPING = {
"i": "Idioms",
"r": "Reference",
"p": "Polysemy",
"n": "Negation",
"q": "Quantitative",
"o": "Others",
}
class CodahConfig(datasets.BuilderConfig):
"""BuilderConfig for CODAH."""
def __init__(self, fold=None, **kwargs):
"""BuilderConfig for CODAH.
Args:
fold: `string`, official cross validation fold.
**kwargs: keyword arguments forwarded to super.
"""
super(CodahConfig, self).__init__(**kwargs)
self.fold = fold
class Codah(datasets.GeneratorBasedBuilder):
"""The COmmonsense Dataset Adversarially-authored by Humans (CODAH)"""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
CodahConfig(name="codah", version=datasets.Version("1.0.0"), description="Full CODAH dataset", fold=None),
CodahConfig(
name="fold_0", version=datasets.Version("1.0.0"), description="Official CV split (fold_0)", fold="fold_0"
),
CodahConfig(
name="fold_1", version=datasets.Version("1.0.0"), description="Official CV split (fold_1)", fold="fold_1"
),
CodahConfig(
name="fold_2", version=datasets.Version("1.0.0"), description="Official CV split (fold_2)", fold="fold_2"
),
CodahConfig(
name="fold_3", version=datasets.Version("1.0.0"), description="Official CV split (fold_3)", fold="fold_3"
),
CodahConfig(
name="fold_4", version=datasets.Version("1.0.0"), description="Official CV split (fold_4)", fold="fold_4"
),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("int32"),
"question_category": datasets.features.ClassLabel(
names=["Idioms", "Reference", "Polysemy", "Negation", "Quantitative", "Others"]
),
"question_propmt": datasets.Value("string"),
"candidate_answers": datasets.features.Sequence(datasets.Value("string")),
"correct_answer_idx": datasets.Value("int32"),
}
),
supervised_keys=None,
homepage="https://github.com/Websail-NU/CODAH",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
if self.config.name == "codah":
data_file = dl_manager.download(_FULL_DATA_URL)
return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"data_file": data_file})]
base_url = f"{_URL}cv_split/{self.config.fold}/"
_urls = {
"train": base_url + "train.tsv",
"dev": base_url + "dev.tsv",
"test": base_url + "test.tsv",
}
downloaded_files = dl_manager.download_and_extract(_urls)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"data_file": downloaded_files["train"]}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"data_file": downloaded_files["dev"]}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"data_file": downloaded_files["test"]}),
]
def _generate_examples(self, data_file):
with open(data_file, encoding="utf-8") as f:
rows = csv.reader(f, delimiter="\t")
for i, row in enumerate(rows):
question_category = QUESTION_CATEGORIES_MAPPING[row[0]] if row[0] != "" else -1
example = {
"id": i,
"question_category": question_category,
"question_propmt": row[1],
"candidate_answers": row[2:-1],
"correct_answer_idx": int(row[-1]),
}
yield i, example
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