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reclor / reclor.py

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


	import json
	import os

	import datasets


	# TODO(reclor): BibTeX citation
	_CITATION = """\
	@inproceedings{yu2020reclor,
	author = {Yu, Weihao and Jiang, Zihang and Dong, Yanfei and Feng, Jiashi},
	title = {ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning},
	booktitle = {International Conference on Learning Representations (ICLR)},
	month = {April},
	year = {2020}
	}

	"""

	# TODO(reclor):
	_DESCRIPTION = """\
	Logical reasoning is an important ability to examine, analyze, and critically evaluate arguments as they occur in ordinary
	language as the definition from LSAC. ReClor is a dataset extracted from logical reasoning questions of standardized graduate
	admission examinations. Empirical results show that the state-of-the-art models struggle on ReClor with poor performance
	indicating more research is needed to essentially enhance the logical reasoning ability of current models. We hope this
	dataset could help push Machine Reading Comprehension (MRC) towards more complicated reasonin
	"""


	class Reclor(datasets.GeneratorBasedBuilder):
	"""TODO(reclor): Short description of my dataset."""

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

	@property
	def manual_download_instructions(self):
	return """\
	to use ReClor you need to download it manually. Please go to its homepage (http://whyu.me/reclor/) fill the google
	form and you will receive a download link and a password to extract it.Please extract all files in one folder and use the path folder in datasets.load_dataset('reclor', data_dir='path/to/folder/folder_name')
	"""

	def _info(self):
	# TODO(reclor): 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 ...
	"context": datasets.Value("string"),
	"question": datasets.Value("string"),
	"answers": datasets.features.Sequence(datasets.Value("string")),
	"label": datasets.Value("string"),
	"id_string": 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="http://whyu.me/reclor/",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	# TODO(reclor): Downloads the data and defines the splits
	# dl_manager is a datasets.download.DownloadManager that can be used to
	# download and extract URLs
	data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))

	if not os.path.exists(data_dir):
	raise FileNotFoundError(
	f"{data_dir} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('wikihow', data_dir=...)` that includes files unzipped from the reclor zip. Manual download instructions: {self.manual_download_instructions}"
	)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": os.path.join(data_dir, "train.json")},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": os.path.join(data_dir, "test.json")},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": os.path.join(data_dir, "val.json")},
	),
	]

	def _generate_examples(self, filepath):
	"""Yields examples."""
	# TODO(reclor): Yields (key, example) tuples from the dataset
	with open(filepath, encoding="utf-8") as f:
	data = json.load(f)
	for id_, row in enumerate(data):
	yield id_, {
	"context": row["context"],
	"question": row["question"],
	"answers": row["answers"],
	"label": str(row.get("label", "")),
	"id_string": row["id_string"],
	}