Datasets:

allenai
/

art

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

	from __future__ import absolute_import, division, print_function

	import json
	import os

	import datasets


	# TODO(art): BibTeX citation
	_CITATION = """\
	@InProceedings{anli,
	author = {Chandra, Bhagavatula and Ronan, Le Bras and Chaitanya, Malaviya and Keisuke, Sakaguchi and Ari, Holtzman
	and Hannah, Rashkin and Doug, Downey and Scott, Wen-tau Yih and Yejin, Choi},
	title = {Abductive Commonsense Reasoning},
	year = {2020}
	}"""

	# TODO(art):
	_DESCRIPTION = """\
	the Abductive Natural Language Inference Dataset from AI2
	"""
	_DATA_URL = "https://storage.googleapis.com/ai2-mosaic/public/alphanli/alphanli-train-dev.zip"


	class ArtConfig(datasets.BuilderConfig):
	"""BuilderConfig for Art."""

	def __init__(self, **kwargs):
	"""BuilderConfig for Art.
	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(ArtConfig, self).__init__(version=datasets.Version("0.1.0", ""), **kwargs)


	class Art(datasets.GeneratorBasedBuilder):
	"""TODO(art): Short description of my dataset."""

	# TODO(art): Set up version.
	VERSION = datasets.Version("0.1.0")
	BUILDER_CONFIGS = [
	ArtConfig(
	name="anli",
	description="""\
	the Abductive Natural Language Inference Dataset from AI2.
	""",
	),
	]

	def _info(self):
	# TODO(art): 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(
	{
	"observation_1": datasets.Value("string"),
	"observation_2": datasets.Value("string"),
	"hypothesis_1": datasets.Value("string"),
	"hypothesis_2": datasets.Value("string"),
	"label": datasets.features.ClassLabel(num_classes=3)
	# 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/anli/submissions/get-started",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	# TODO(art): 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(_DATA_URL)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"filepath": os.path.join(dl_dir, "dev.jsonl"),
	"labelpath": os.path.join(dl_dir, "dev-labels.lst"),
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepath": os.path.join(dl_dir, "train.jsonl"),
	"labelpath": os.path.join(dl_dir, "train-labels.lst"),
	},
	),
	]

	def _generate_examples(self, filepath, labelpath):
	"""Yields examples."""
	# TODO(art): Yields (key, example) tuples from the dataset
	data = []
	for line in open(filepath, encoding="utf-8"):
	data.append(json.loads(line))
	labels = []
	with open(labelpath, encoding="utf-8") as f:
	for word in f:
	labels.append(word)
	for idx, row in enumerate(data):
	yield idx, {
	"observation_1": row["obs1"],
	"observation_2": row["obs2"],
	"hypothesis_1": row["hyp1"],
	"hypothesis_2": row["hyp2"],
	"label": labels[idx],
	}