Datasets:

shlomihod
/

civil-comments-wilds

	# 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.
	"""CivilComments WILDS"""


	import csv

	import datasets


	_CITATION = """\
	@inproceedings{wilds2021,
	title = {{WILDS}: A Benchmark of in-the-Wild Distribution Shifts},
	author = {Pang Wei Koh and Shiori Sagawa and Henrik Marklund and Sang Michael Xie and Marvin Zhang and
	Akshay Balsubramani and Weihua Hu and Michihiro Yasunaga and Richard Lanas Phillips and Irena Gao and
	Tony Lee and Etienne David and Ian Stavness and Wei Guo and Berton A. Earnshaw and Imran S. Haque and
	Sara Beery and Jure Leskovec and Anshul Kundaje and Emma Pierson and Sergey Levine and Chelsea Finn
	and Percy Liang},
	booktitle = {International Conference on Machine Learning (ICML)},
	year = {2021}
	}

	@inproceedings{borkan2019nuanced,
	title={Nuanced metrics for measuring unintended bias with real data for text classification},
	author={Borkan, Daniel and Dixon, Lucas and Sorensen, Jeffrey and Thain, Nithum and Vasserman, Lucy},
	booktitle={Companion Proceedings of The 2019 World Wide Web Conference},
	pages={491--500},
	year={2019}
	}

	@article{DBLP:journals/corr/abs-2211-09110,
	author = {Percy Liang and
	Rishi Bommasani and
	Tony Lee and
	Dimitris Tsipras and
	Dilara Soylu and
	Michihiro Yasunaga and
	Yian Zhang and
	Deepak Narayanan and
	Yuhuai Wu and
	Ananya Kumar and
	Benjamin Newman and
	Binhang Yuan and
	Bobby Yan and
	Ce Zhang and
	Christian Cosgrove and
	Christopher D. Manning and
	Christopher R{\'{e}} and
	Diana Acosta{-}Navas and
	Drew A. Hudson and
	Eric Zelikman and
	Esin Durmus and
	Faisal Ladhak and
	Frieda Rong and
	Hongyu Ren and
	Huaxiu Yao and
	Jue Wang and
	Keshav Santhanam and
	Laurel J. Orr and
	Lucia Zheng and
	Mert Y{\"{u}}ksekg{\"{o}}n{\"{u}}l and
	Mirac Suzgun and
	Nathan Kim and
	Neel Guha and
	Niladri S. Chatterji and
	Omar Khattab and
	Peter Henderson and
	Qian Huang and
	Ryan Chi and
	Sang Michael Xie and
	Shibani Santurkar and
	Surya Ganguli and
	Tatsunori Hashimoto and
	Thomas Icard and
	Tianyi Zhang and
	Vishrav Chaudhary and
	William Wang and
	Xuechen Li and
	Yifan Mai and
	Yuhui Zhang and
	Yuta Koreeda},
	title = {Holistic Evaluation of Language Models},
	journal = {CoRR},
	volume = {abs/2211.09110},
	year = {2022},
	url = {https://doi.org/10.48550/arXiv.2211.09110},
	doi = {10.48550/arXiv.2211.09110},
	eprinttype = {arXiv},
	eprint = {2211.09110},
	timestamp = {Wed, 23 Nov 2022 18:03:56 +0100},
	biburl = {https://dblp.org/rec/journals/corr/abs-2211-09110.bib},
	bibsource = {dblp computer science bibliography, https://dblp.org}
	}
	"""


	_DESCRIPTION = """\
	In this dataset, given a textual dialogue i.e. an utterance along with two previous turns of context, the goal was to infer the underlying emotion of the utterance by choosing from four emotion classes - Happy, Sad, Angry and Others.
	"""


	class CivilCommentsWILDSConfig(datasets.BuilderConfig):
	"""BuilderConfig for CivilCommentsWILDS."""

	def __init__(self, name, **kwargs):
	"""BuilderConfig for EmoContext.
	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(CivilCommentsWILDSConfig, self).__init__(**kwargs)
	self.name = name


	# _URL = (
	# "https://worksheets.codalab.org/rest/bundles/0x8cd3de0634154aeaad2ee6eb96723c6e/"
	# "contents/blob/all_data_with_identities.csv"
	# )


	_URL = "all_data_with_identities.csv"


	class CivilCommentsWILDS(datasets.GeneratorBasedBuilder):
	"""SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text. Version 1.0.0"""

	VERSION = datasets.Version("1.0.0")

	ALL_DEMOGRAPHICS = "all"

	DEMOGRAPHICS = {"male", "female", "LGBTQ", "christian", "muslim", "other_religions", "black", "white"}

	DEMOGRAPHICS_COLUMN_INDEX = {
	"male": 21,
	"female": 22,
	"LGBTQ": 47,
	"christian": 29,
	"muslim": 31,
	"other_religions": 48,
	"black": 36,
	"white": 37
	}

	BUILDER_CONFIGS = [
	CivilCommentsWILDSConfig(
	name=name,
	version=datasets.Version("1.0.0"),
	description="Plain text",
	)
	for name in DEMOGRAPHICS \| {ALL_DEMOGRAPHICS}
	]

	DEFAULT_CONFIG_NAME = ALL_DEMOGRAPHICS

	LABERLS = ["False", "True"]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"text": datasets.Value("string"),
	"label": datasets.features.ClassLabel(names=CivilCommentsWILDS.LABERLS),
	}
	),
	supervised_keys=None,
	homepage="https://wilds.stanford.edu/datasets/#civilcomments",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	downloaded_file = dl_manager.download_and_extract(_URL)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"filepath": downloaded_file, "split": "train"},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={"filepath": downloaded_file, "split": "val"},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={"filepath": downloaded_file, "split": "test"},
	),
	]

	def _generate_examples(self, filepath, split):
	"""Yields examples."""
	# Based on HELM's code
	# https://github.com/stanford-crfm/helm/blob/abfdcd8acd23b5ef3ec7ec987f5c90fb9de81406/src/helm/benchmark/scenarios/civil_comments_scenario.py#L20
	demographic = self.config.name
	with open(filepath, "r") as f:
	data = csv.reader(f, delimiter=",")
	next(data, None)
	for id_, row in enumerate(data):
	if row[3] == split:
	if (demographic == CivilCommentsWILDS.ALL_DEMOGRAPHICS
	or float(row[CivilCommentsWILDS.DEMOGRAPHICS_COLUMN_INDEX[demographic]]) >= 0.5):
	yield id_, {
	"text": row[2],
	"label": int(float(row[14]) >= 0.5),
	}