Datasets:

allenai
/

quartz

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


	import json
	import os

	import datasets


	# TODO(quartz): BibTeX citation
	_CITATION = """\
	@InProceedings{quartz,
	author = {Oyvind Tafjord and Matt Gardner and Kevin Lin and Peter Clark},
	title = {"QUARTZ: An Open-Domain Dataset of Qualitative Relationship
	Questions"},
	year = {"2019"},
	}
	"""

	# TODO(quartz):
	_DESCRIPTION = """\
	QuaRTz is a crowdsourced dataset of 3864 multiple-choice questions about open domain qualitative relationships. Each
	question is paired with one of 405 different background sentences (sometimes short paragraphs).
	The QuaRTz dataset V1 contains 3864 questions about open domain qualitative relationships. Each question is paired with
	one of 405 different background sentences (sometimes short paragraphs).
	The dataset is split into train (2696), dev (384) and test (784). A background sentence will only appear in a single split.
	"""

	_URL = "https://s3-us-west-2.amazonaws.com/ai2-website/data/quartz-dataset-v1-aug2019.zip"


	class Quartz(datasets.GeneratorBasedBuilder):
	"""TODO(quartz): Short description of my dataset."""

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

	def _info(self):
	# TODO(quartz): 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(
	{
	# These are the features of your dataset like images, labels ...
	"id": datasets.Value("string"),
	"question": datasets.Value("string"),
	"choices": datasets.features.Sequence(
	{"text": datasets.Value("string"), "label": datasets.Value("string")}
	),
	"answerKey": datasets.Value("string"),
	"para": datasets.Value("string"),
	"para_id": datasets.Value("string"),
	"para_anno": {
	"effect_prop": datasets.Value("string"),
	"cause_dir_str": datasets.Value("string"),
	"effect_dir_str": datasets.Value("string"),
	"cause_dir_sign": datasets.Value("string"),
	"effect_dir_sign": datasets.Value("string"),
	"cause_prop": datasets.Value("string"),
	},
	"question_anno": {
	"more_effect_dir": datasets.Value("string"),
	"less_effect_dir": datasets.Value("string"),
	"less_cause_prop": datasets.Value("string"),
	"more_effect_prop": datasets.Value("string"),
	"less_effect_prop": datasets.Value("string"),
	"less_cause_dir": datasets.Value("string"),
	},
	}
	),
	# 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/quartz",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	# TODO(quartz): 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, "quartz-dataset-v1-aug2019")
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": os.path.join(data_dir, "train.jsonl")},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": os.path.join(data_dir, "test.jsonl")},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": os.path.join(data_dir, "dev.jsonl")},
	),
	]

	def _generate_examples(self, filepath):
	"""Yields examples."""
	# TODO(quartz): Yields (key, example) tuples from the dataset
	with open(filepath, encoding="utf-8") as f:
	for row in f:
	data = json.loads(row)
	id_ = data["id"]
	question = data["question"]["stem"]
	answerKey = data["answerKey"]
	choices = data["question"]["choices"]
	choice_text = [choice["text"] for choice in choices]
	choice_label = [choice["label"] for choice in choices]
	para_id = data["para_id"]
	para = data["para"]
	para_ano = data["para_anno"]
	effect_prop = para_ano.get("effect_prop", "")
	cause_dir_str = para_ano.get("cause_dir_str", "")
	effect_dir_str = para_ano.get("effect_dir_str", "")
	cause_dir_sign = para_ano.get("cause_dir_sign", "")
	effect_dir_sign = para_ano.get("effect_dir_sign", "")
	cause_prop = para_ano.get("cause_prop", "")
	question_anno = data["question_anno"]
	more_effect_dir = "" if not question_anno else question_anno.get("more_effect_dir", "")
	less_effect_dir = "" if not question_anno else question_anno.get("less_effect_dir", "")
	less_cause_prop = "" if not question_anno else question_anno.get("less_cause_prop", "")
	more_effect_prop = "" if not question_anno else question_anno.get("more_effect_prop", "")
	less_effect_prop = "" if not question_anno else question_anno.get("less_effect_prop", "")
	less_cause_dir = "" if not question_anno else question_anno.get("less_effect_prop", "")
	yield id_, {
	"id": id_,
	"question": question,
	"choices": {"text": choice_text, "label": choice_label},
	"answerKey": answerKey,
	"para": para,
	"para_id": para_id,
	"para_anno": {
	"effect_prop": effect_prop,
	"cause_dir_str": cause_dir_str,
	"effect_dir_str": effect_dir_str,
	"cause_dir_sign": cause_dir_sign,
	"effect_dir_sign": effect_dir_sign,
	"cause_prop": cause_prop,
	},
	"question_anno": {
	"more_effect_dir": more_effect_dir,
	"less_effect_dir": less_effect_dir,
	"less_cause_prop": less_cause_prop,
	"more_effect_prop": more_effect_prop,
	"less_effect_prop": less_effect_prop,
	"less_cause_dir": less_cause_dir,
	},
	}