Datasets:

jaredfern
/

codah

	# 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