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winogrande / winogrande.py

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Update files from the datasets library (from 1.16.0)

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


	import json
	import os

	import datasets


	# TODO(winogrande): BibTeX citation
	_CITATION = """\
	@InProceedings{ai2:winogrande,
	title = {WinoGrande: An Adversarial Winograd Schema Challenge at Scale},
	authors={Keisuke, Sakaguchi and Ronan, Le Bras and Chandra, Bhagavatula and Yejin, Choi
	},
	year={2019}
	}
	"""

	# TODO(winogrande):
	_DESCRIPTION = """\
	WinoGrande is a new collection of 44k problems, inspired by Winograd Schema Challenge (Levesque, Davis, and Morgenstern
	2011), but adjusted to improve the scale and robustness against the dataset-specific bias. Formulated as a
	fill-in-a-blank task with binary options, the goal is to choose the right option for a given sentence which requires
	commonsense reasoning.
	"""

	_URL = "https://storage.googleapis.com/ai2-mosaic/public/winogrande/winogrande_1.1.zip"
	_FORMATS = ["xs", "s", "m", "l", "xl", "debiased"]


	class WinograndeConfig(datasets.BuilderConfig):

	"""BuilderConfig for Discofuse"""

	def __init__(self, data_size, **kwargs):
	"""

	Args:
	data_size: the format of the training set we want to use (xs, s, m, l, xl, debiased)
	**kwargs: keyword arguments forwarded to super.
	"""
	super(WinograndeConfig, self).__init__(version=datasets.Version("1.1.0", ""), **kwargs)
	self.data_size = data_size


	class Winogrande(datasets.GeneratorBasedBuilder):
	"""TODO(winogrande): Short description of my dataset."""

	# TODO(winogrande): Set up version.
	VERSION = datasets.Version("1.1.0")
	BUILDER_CONFIGS = [
	WinograndeConfig(name="winogrande_" + data_size, description="AI2 dataset", data_size=data_size)
	for data_size in _FORMATS
	]

	def _info(self):
	# TODO(winogrande): 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(
	{
	"sentence": datasets.Value("string"),
	"option1": datasets.Value("string"),
	"option2": datasets.Value("string"),
	"answer": 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://leaderboard.allenai.org/winogrande/submissions/get-started",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	# TODO(winogrande): 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, "winogrande_1.1")
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir, f"train_{self.config.data_size}.jsonl"),
	# 'labelpath': os.path.join(data_dir, 'train_{}-labels.lst'.format(self.config.data_size)),
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": os.path.join(data_dir, "test.jsonl"), "split": "test"},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir, "dev.jsonl"),
	# 'labelpath': os.path.join(data_dir, 'dev-labels.lst'),
	"split": "dev",
	},
	),
	]

	def _generate_examples(self, filepath, split):
	"""Yields examples."""
	# TODO(winogrande): Yields (key, example) tuples from the dataset
	with open(filepath, encoding="utf-8") as f:
	for id_, row in enumerate(f):
	data = json.loads(row)
	if split == "test":
	yield id_, {
	"sentence": data["sentence"],
	"option1": data["option1"],
	"option2": data["option2"],
	"answer": "",
	}
	else:
	yield id_, {
	"sentence": data["sentence"],
	"option1": data["option1"],
	"option2": data["option2"],
	"answer": data["answer"],
	}


	# def _generate_test_example(filepath, split, labelpath=None):
	# with open(filepath, encoding="utf-8") as f:
	# for id_, row in enumerate(f):
	# data = json.loads(row)
	# yield id_,{
	# 'sentence': data['sentence'],
	# 'option1': data['option1'],
	# 'option2': data['option2'],
	# 'answer': None
	# }