Datasets:

uclanlp
/

wino_bias

	# coding=utf-8
	# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
	#
	# 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.
	"""WinoBias: Winograd-schema dataset for detecting gender bias"""

	from __future__ import absolute_import, division, print_function

	import datasets


	_CITATION = """\
	@article{DBLP:journals/corr/abs-1804-06876,
	author = {Jieyu Zhao and
	Tianlu Wang and
	Mark Yatskar and
	Vicente Ordonez and
	Kai{-}Wei Chang},
	title = {Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods},
	journal = {CoRR},
	volume = {abs/1804.06876},
	year = {2018},
	url = {http://arxiv.org/abs/1804.06876},
	archivePrefix = {arXiv},
	eprint = {1804.06876},
	timestamp = {Mon, 13 Aug 2018 16:47:01 +0200},
	biburl = {https://dblp.org/rec/journals/corr/abs-1804-06876.bib},
	bibsource = {dblp computer science bibliography, https://dblp.org}
	}
	"""

	_DESCRIPTION = """\
	WinoBias, a Winograd-schema dataset for coreference resolution focused on gender bias.
	The corpus contains Winograd-schema style sentences with entities corresponding to people
	referred by their occupation (e.g. the nurse, the doctor, the carpenter).
	"""

	_HOMEPAGE = "https://uclanlp.github.io/corefBias/overview"

	_LICENSE = "MIT License (https://github.com/uclanlp/corefBias/blob/master/LICENSE)"

	_URL = "https://drive.google.com/uc?export=download&id=14Im3BnNl-d2fYETYmiH5yq6eFGLVC3g0"


	class WinoBias(datasets.GeneratorBasedBuilder):
	"""WinoBias: Winograd-schema dataset for detecting gender bias"""

	VERSION = datasets.Version("4.0.0")

	# This is an example of a dataset with multiple configurations.
	# If you don't want/need to define several sub-sets in your dataset,
	# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

	# If you need to make complex sub-parts in the datasets with configurable options
	# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
	# BUILDER_CONFIG_CLASS = MyBuilderConfig

	# You will be able to load one or the other configurations in the following list with
	# data = datasets.load_dataset('my_dataset', 'first_domain')
	# data = datasets.load_dataset('my_dataset', 'second_domain')
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="wino_bias",
	version=VERSION,
	description="WinoBias: Winograd-schema dataset for detecting gender bias",
	),
	]

	def _info(self):
	return datasets.DatasetInfo(
	# This is the description that will appear on the datasets page.
	description=_DESCRIPTION,
	# This defines the different columns of the dataset and their types
	# Info about features for this: http://cemantix.org/data/ontonotes.html
	features=datasets.Features(
	{
	"document_id": datasets.Value("string"),
	"part_number": datasets.Value("string"),
	"word_number": datasets.Sequence(datasets.Value("int32")),
	"tokens": datasets.Sequence(datasets.Value("string")),
	"pos_tags": datasets.Sequence(
	datasets.features.ClassLabel(
	names=[
	'"',
	"''",
	"#",
	"$",
	"(",
	")",
	",",
	".",
	":",
	"``",
	"CC",
	"CD",
	"DT",
	"EX",
	"FW",
	"IN",
	"JJ",
	"JJR",
	"JJS",
	"LS",
	"MD",
	"NN",
	"NNP",
	"NNPS",
	"NNS",
	"NN\|SYM",
	"PDT",
	"POS",
	"PRP",
	"PRP$",
	"RB",
	"RBR",
	"RBS",
	"RP",
	"SYM",
	"TO",
	"UH",
	"VB",
	"VBD",
	"VBG",
	"VBN",
	"VBP",
	"VBZ",
	"WDT",
	"WP",
	"WP$",
	"WRB",
	"HYPH",
	"XX",
	"NFP",
	"AFX",
	"ADD",
	"-LRB-",
	"-RRB-",
	]
	)
	),
	"parse_bit": datasets.Sequence(datasets.Value("string")),
	"predicate_lemma": datasets.Sequence(datasets.Value("string")),
	"predicate_framenet_id": datasets.Sequence(datasets.Value("string")),
	"word_sense": datasets.Sequence(datasets.Value("string")),
	"speaker": datasets.Sequence(datasets.Value("string")),
	"ner_tags": datasets.Sequence(
	datasets.features.ClassLabel(
	names=[
	"B-PERSON",
	"I-PERSON",
	"B-NORP",
	"I-NORP",
	"B-FAC",
	"I-FAC",
	"B-ORG",
	"I-ORG",
	"B-GPE",
	"I-GPE",
	"B-LOC",
	"I-LOC",
	"B-PRODUCT",
	"I-PRODUCT",
	"B-EVENT",
	"I-EVENT",
	"B-WORK_OF_ART",
	"I-WORK_OF_ART",
	"B-LAW",
	"I-LAW",
	"B-LANGUAGE",
	"I-LANGUAGE",
	"B-DATE",
	"I-DATE",
	"B-TIME",
	"I-TIME",
	"B-PERCENT",
	"I-PERCENT",
	"B-MONEY",
	"I-MONEY",
	"B-QUANTITY",
	"I-QUANTITY",
	"B-ORDINAL",
	"I-ORDINAL",
	"B-CARDINAL",
	"I-CARDINAL",
	"*",
	"0",
	]
	)
	),
	"verbal_predicates": datasets.Sequence(datasets.Value("string")),
	}
	),
	supervised_keys=None,
	# Homepage of the dataset for documentation
	homepage=_HOMEPAGE,
	# License for the dataset if available
	license=_LICENSE,
	# Citation for the dataset
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	data_dir = dl_manager.download_and_extract(_URL)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": data_dir},
	)
	]

	def _generate_examples(self, filepath):
	""" Yields examples. """
	with open(filepath, encoding="utf-8") as f:
	id_ = 0
	document_id = None
	part_number = 0
	word_num = []
	tokens = []
	pos_tags = []
	parse_bit = []
	predicate_lemma = []
	predicate_framenet_id = []
	word_sense = []
	speaker = []
	ner_tags = []
	ner_start = False
	verbal_predicates = []
	for line in f:
	if line.startswith("#begin") or line.startswith("#end"):
	continue
	elif not line.strip():
	id_ += 1
	yield str(id_), {
	"document_id": document_id,
	"part_number": part_number,
	"word_number": word_num,
	"tokens": tokens,
	"pos_tags": pos_tags,
	"parse_bit": parse_bit,
	"predicate_lemma": predicate_lemma,
	"predicate_framenet_id": predicate_framenet_id,
	"word_sense": word_sense,
	"speaker": speaker,
	"ner_tags": ner_tags,
	"verbal_predicates": verbal_predicates,
	}
	word_num = []
	tokens = []
	pos_tags = []
	parse_bit = []
	predicate_lemma = []
	predicate_framenet_id = []
	word_sense = []
	speaker = []
	ner_tags = []
	verbal_predicates = []
	else:
	splits = [s for s in line.split(" ") if s]
	if len(splits) > 7:
	document_id = splits[0]
	part_number = splits[1]
	word_num.append(splits[2])
	tokens.append(splits[3])
	pos_tags.append(splits[4])
	parse_bit.append(splits[5])
	predicate_lemma.append(splits[6])
	predicate_framenet_id.append(splits[7])
	word_sense.append(splits[8])
	speaker.append(splits[9])
	ner_word = splits[10]
	if ")" in ner_word and ner_start:
	ner_start = False
	ner_word = "0"
	if "(" in ner_word:
	ner_start = True
	ner_word = ner_word.strip(" ").replace("(", "B-").replace("*", "").replace(")", "")
	start_word = ner_word.strip(" ").replace("B-", "")
	if ner_start:
	if ner_word.strip(" ") == "*":
	ner_word = "I-" + start_word
	ner_tags.append(ner_word)
	word_is_verbal_predicate = any(["(V" in x for x in splits[11:-1]])
	if word_is_verbal_predicate:
	verbal_predicates.append(splits[3])
	if tokens:
	# add the last one
	id_ += 1
	yield str(id_), {
	"document_id": document_id,
	"part_number": part_number,
	"word_number": word_num,
	"tokens": tokens,
	"pos_tags": pos_tags,
	"parse_bit": parse_bit,
	"predicate_lemma": predicate_lemma,
	"predicate_framenet_id": predicate_framenet_id,
	"word_sense": word_sense,
	"speaker": speaker,
	"ner_tags": ner_tags,
	"verbal_predicates": verbal_predicates,
	}