Datasets:

allenai
/

qasc

	"""TODO(qasc): Add a description here."""


	import json

	import datasets


	# TODO(qasc): BibTeX citation
	_CITATION = """\
	@article{allenai:qasc,
	author = {Tushar Khot and Peter Clark and Michal Guerquin and Peter Jansen and Ashish Sabharwal},
	title = {QASC: A Dataset for Question Answering via Sentence Composition},
	journal = {arXiv:1910.11473v2},
	year = {2020},
	}
	"""

	# TODO(qasc):
	_DESCRIPTION = """
	QASC is a question-answering dataset with a focus on sentence composition. It consists of 9,980 8-way multiple-choice
	questions about grade school science (8,134 train, 926 dev, 920 test), and comes with a corpus of 17M sentences.
	"""
	_URl = "http://data.allenai.org/downloads/qasc/qasc_dataset.tar.gz"


	class Qasc(datasets.GeneratorBasedBuilder):
	"""TODO(qasc): Short description of my dataset."""

	# TODO(qasc): Set up version.
	VERSION = datasets.Version("0.1.0")

	def _info(self):
	# TODO(qasc): Specifies the datasets.DatasetInfo object
	return datasets.DatasetInfo(
	# This is the description that will appear on the datasets page.
	description=_DESCRIPTION,
	# datasets.features.FeatureConnectors
	features=datasets.Features(
	{
	"id": datasets.Value("string"),
	"question": datasets.Value("string"),
	"choices": datasets.features.Sequence(
	{"text": datasets.Value("string"), "label": datasets.Value("string")}
	),
	"answerKey": datasets.Value("string"),
	"fact1": datasets.Value("string"),
	"fact2": datasets.Value("string"),
	"combinedfact": datasets.Value("string"),
	"formatted_question": datasets.Value("string"),
	# These are the features of your dataset like images, labels ...
	}
	),
	# If there's a common (input, target) tuple from the features,
	# specify them here. They'll be used if as_supervised=True in
	# builder.as_dataset.
	supervised_keys=None,
	# Homepage of the dataset for documentation
	homepage="https://allenai.org/data/qasc",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	# TODO(qasc): Downloads the data and defines the splits
	# dl_manager is a datasets.download.DownloadManager that can be used to
	# download and extract URLs
	archive = dl_manager.download(_URl)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": "/".join(["QASC_Dataset", "train.jsonl"]),
	"files": dl_manager.iter_archive(archive),
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": "/".join(["QASC_Dataset", "test.jsonl"]),
	"files": dl_manager.iter_archive(archive),
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": "/".join(["QASC_Dataset", "dev.jsonl"]),
	"files": dl_manager.iter_archive(archive),
	},
	),
	]

	def _generate_examples(self, filepath, files):
	"""Yields examples."""
	# TODO(qasc): Yields (key, example) tuples from the dataset
	for path, f in files:
	if path == filepath:
	for row in f:
	data = json.loads(row.decode("utf-8"))
	answerkey = data.get("answerKey", "")
	id_ = data["id"]
	question = data["question"]["stem"]
	choices = data["question"]["choices"]
	text_choices = [choice["text"] for choice in choices]
	label_choices = [choice["label"] for choice in choices]
	fact1 = data.get("fact1", "")
	fact2 = data.get("fact2", "")
	combined_fact = data.get("combinedfact", "")
	formatted_question = data.get("formatted_question", "")
	yield id_, {
	"id": id_,
	"answerKey": answerkey,
	"question": question,
	"choices": {"text": text_choices, "label": label_choices},
	"fact1": fact1,
	"fact2": fact2,
	"combinedfact": combined_fact,
	"formatted_question": formatted_question,
	}
	break