Datasets:

allenai
/

ai2_arc

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

	from __future__ import absolute_import, division, print_function

	import json
	import os

	import datasets


	# TODO(ai2_arc): BibTeX citation
	_CITATION = """\
	@article{allenai:arc,
	author = {Peter Clark and Isaac Cowhey and Oren Etzioni and Tushar Khot and
	Ashish Sabharwal and Carissa Schoenick and Oyvind Tafjord},
	title = {Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge},
	journal = {arXiv:1803.05457v1},
	year = {2018},
	}
	"""

	# TODO(ai2_arc):
	_DESCRIPTION = """\
	A new dataset of 7,787 genuine grade-school level, multiple-choice science questions, assembled to encourage research in
	advanced question-answering. The dataset is partitioned into a Challenge Set and an Easy Set, where the former contains
	only questions answered incorrectly by both a retrieval-based algorithm and a word co-occurrence algorithm. We are also
	including a corpus of over 14 million science sentences relevant to the task, and an implementation of three neural baseline models for this dataset. We pose ARC as a challenge to the community.
	"""

	_URL = "https://s3-us-west-2.amazonaws.com/ai2-website/data/ARC-V1-Feb2018.zip"


	class Ai2ArcConfig(datasets.BuilderConfig):
	"""BuilderConfig for Ai2ARC."""

	def __init__(self, **kwargs):
	"""BuilderConfig for Ai2Arc.

	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(Ai2ArcConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)


	class Ai2Arc(datasets.GeneratorBasedBuilder):
	"""TODO(arc): Short description of my dataset."""

	# TODO(arc): Set up version.
	VERSION = datasets.Version("1.0.0")
	BUILDER_CONFIGS = [
	Ai2ArcConfig(
	name="ARC-Challenge",
	description="""\
	Challenge Set of 2590 “hard” questions (those that both a retrieval and a co-occurrence method fail to answer correctly)
	""",
	),
	Ai2ArcConfig(
	name="ARC-Easy",
	description="""\
	Easy Set of 5197 questions
	""",
	),
	]

	def _info(self):
	# TODO(ai2_arc): 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")
	# 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/arc",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	# TODO(ai2_arc): Downloads the data and defines the splits
	# dl_manager is a datasets.download.DownloadManager that can be used to
	# download and extract URLs
	dl_dir = dl_manager.download_and_extract(_URL)
	data_dir = os.path.join(dl_dir, "ARC-V1-Feb2018-2")
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": os.path.join(data_dir, self.config.name, self.config.name + "-Train.jsonl")},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": os.path.join(data_dir, self.config.name, self.config.name + "-Test.jsonl")},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": os.path.join(data_dir, self.config.name, self.config.name + "-Dev.jsonl")},
	),
	]

	def _generate_examples(self, filepath):
	"""Yields examples."""
	# TODO(ai2_arc): Yields (key, example) tuples from the dataset
	with open(filepath, encoding="utf-8") as f:
	for row in f:
	data = json.loads(row)
	answerkey = data["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]
	yield id_, {
	"id": id_,
	"answerKey": answerkey,
	"question": question,
	"choices": {"text": text_choices, "label": label_choices},
	}