Datasets:
Tasks:
Question Answering
Formats:
parquet
Sub-tasks:
multiple-choice-qa
Languages:
English
Size:
10K - 100K
ArXiv:
License:
File size: 5,731 Bytes
f2fe64f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 |
# 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)"""
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
|