Datasets:
hans

	# coding=utf-8
	# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.

	# Lint as: python3
	"""Heuristic Analysis for NLI Systems"""


	import datasets


	_CITATION = """\
	@article{DBLP:journals/corr/abs-1902-01007,
	author = {R. Thomas McCoy and
	Ellie Pavlick and
	Tal Linzen},
	title = {Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural
	Language Inference},
	journal = {CoRR},
	volume = {abs/1902.01007},
	year = {2019},
	url = {http://arxiv.org/abs/1902.01007},
	archivePrefix = {arXiv},
	eprint = {1902.01007},
	timestamp = {Tue, 21 May 2019 18:03:36 +0200},
	biburl = {https://dblp.org/rec/journals/corr/abs-1902-01007.bib},
	bibsource = {dblp computer science bibliography, https://dblp.org}
	}
	"""

	_DESCRIPTION = """\
	The HANS dataset is an NLI evaluation set that tests specific hypotheses about invalid heuristics that NLI models are likely to learn.
	"""


	class HansConfig(datasets.BuilderConfig):
	"""BuilderConfig for HANS."""

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

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


	class Hans(datasets.GeneratorBasedBuilder):
	"""Hans: Heuristic Analysis for NLI Systems."""

	BUILDER_CONFIGS = [
	HansConfig(
	name="plain_text",
	description="Plain text",
	),
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"premise": datasets.Value("string"),
	"hypothesis": datasets.Value("string"),
	"label": datasets.features.ClassLabel(names=["entailment", "non-entailment"]),
	"parse_premise": datasets.Value("string"),
	"parse_hypothesis": datasets.Value("string"),
	"binary_parse_premise": datasets.Value("string"),
	"binary_parse_hypothesis": datasets.Value("string"),
	"heuristic": datasets.Value("string"),
	"subcase": datasets.Value("string"),
	"template": datasets.Value("string"),
	}
	),
	# No default supervised_keys (as we have to pass both premise
	# and hypothesis as input).
	supervised_keys=None,
	homepage="https://github.com/tommccoy1/hans",
	citation=_CITATION,
	)

	def _vocab_text_gen(self, filepath):
	for _, ex in self._generate_examples(filepath):
	yield " ".join([ex["premise"], ex["hypothesis"]])

	def _split_generators(self, dl_manager):

	train_path = dl_manager.download_and_extract(
	"https://raw.githubusercontent.com/tommccoy1/hans/master/heuristics_train_set.txt"
	)
	valid_path = dl_manager.download_and_extract(
	"https://raw.githubusercontent.com/tommccoy1/hans/master/heuristics_evaluation_set.txt"
	)

	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
	datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": valid_path}),
	]

	def _generate_examples(self, filepath):
	"""Generate hans examples.

	Args:
	filepath: a string

	Yields:
	dictionaries containing "premise", "hypothesis" and "label" strings
	"""
	for idx, line in enumerate(open(filepath, "r", encoding="utf-8")):
	if idx == 0:
	continue # skip header
	line = line.strip()
	split_line = line.split("\t")
	# Examples not marked with a three out of five consensus are marked with
	# "-" and should not be used in standard evaluations.
	if split_line[0] == "-":
	continue
	# Works for both splits even though dev has some extra human labels.
	yield idx, {
	"premise": split_line[5],
	"hypothesis": split_line[6],
	"label": split_line[0],
	"binary_parse_premise": split_line[1],
	"binary_parse_hypothesis": split_line[2],
	"parse_premise": split_line[3],
	"parse_hypothesis": split_line[4],
	"heuristic": split_line[8],
	"subcase": split_line[9],
	"template": split_line[10],
	}