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SOCKET

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SOCKET / SOCKET.py

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	# 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.
	"""The SOCKET Datasets"""


	import datasets


	_CITATION = """
	@misc{choi2023llms,
	title={Do LLMs Understand Social Knowledge? Evaluating the Sociability of Large Language Models with SocKET Benchmark},
	author={Minje Choi and Jiaxin Pei and Sagar Kumar and Chang Shu and David Jurgens},
	year={2023},
	eprint={2305.14938},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	"""

	_DESCRIPTION = """\
	A unified evaluation benchmark dataset for evaludating socialbility of NLP models.
	"""

	_HOMEPAGE = "https://github.com/minjechoi/SOCKET"

	_LICENSE = ""

	#set up url or the file dir here
	URL = "https://huggingface.co/datasets/Blablablab/SOCKET/resolve/main/SOCKET_DATA/"

	TASK_DICT = {
	'humor_sarcasm': [
	'hahackathon#humor_rating',
	'humor-pairs',
	'sarc',
	'tweet_irony',
	'hahackathon#is_humor',
	],
	'offensive': [
	'contextual-abuse#IdentityDirectedAbuse',
	'contextual-abuse#PersonDirectedAbuse',
	'hahackathon#offense_rating',
	'hasbiasedimplication',
	'hateoffensive',
	'implicit-hate#explicit_hate',
	'implicit-hate#implicit_hate',
	'implicit-hate#incitement_hate',
	'implicit-hate#inferiority_hate',
	'implicit-hate#stereotypical_hate',
	'implicit-hate#threatening_hate',
	'implicit-hate#white_grievance_hate',
	'intentyn',
	'jigsaw#severe_toxic',
	'jigsaw#identity_hate',
	'jigsaw#threat',
	'jigsaw#obscene',
	'jigsaw#insult',
	'jigsaw#toxic',
	'offensiveyn',
	'sexyn',
	'talkdown-pairs',
	'toxic-span',
	'tweet_offensive'
	],
	'sentiment_emotion': [
	'crowdflower',
	'dailydialog',
	'emobank#arousal',
	'emobank#dominance',
	'emobank#valence',
	'emotion-span',
	'empathy#distress',
	'empathy#distress_bin',
	'same-side-pairs',
	'sentitreebank',
	'tweet_emoji',
	'tweet_emotion',
	'tweet_sentiment'
	],
	'social_factors': [
	'complaints',
	'empathy#empathy',
	'empathy#empathy_bin',
	'hayati_politeness',
	'questionintimacy',
	'stanfordpoliteness'
	],
	'trustworthy': [
	'bragging#brag_achievement',
	'bragging#brag_action',
	'bragging#brag_possession',
	'bragging#brag_trait',
	'hypo-l',
	'neutralizing-bias-pairs',
	'propaganda-span',
	'rumor#rumor_bool',
	'two-to-lie#receiver_truth',
	'two-to-lie#sender_truth',
	]
	}

	task2category = {}
	for category, tasks in TASK_DICT.items():
	for task in tasks:
	task2category[task] = category

	TASK_NAMES = []
	for tasks in TASK_DICT.values():
	TASK_NAMES.extend(tasks)
	TASK_NAMES = sorted(TASK_NAMES)

	_URLs = {}
	for task in TASK_NAMES:
	_URLs[task] = {}
	for s in ['train', 'test', 'val', 'sockette']:
	for t in ['text', 'labels']:
	task_url = '%s%s/%s_%s.txt'%(URL,task,s,t)
	task_url = task_url.replace('#','%23')
	_URLs[task][s + '_' + t] = task_url

	class SOCKETConfig(datasets.BuilderConfig):
	def __init__(self, args, type=None, sub_type=None, *kwargs):
	super().__init__(
	*args,
	name=f"{type}",
	**kwargs,
	)
	self.type = type
	self.sub_type = sub_type


	class SOCKET(datasets.GeneratorBasedBuilder):
	"""SOCKET Dataset."""

	BUILDER_CONFIGS = [
	SOCKETConfig(
	type=key,
	sub_type=None,
	version=datasets.Version("1.1.0"),
	description=f"This part of my dataset covers {key} part of the SocKET Dataset.",
	)
	for key in list(TASK_NAMES)
	]

	def _info(self):
	if self.config.type == "questionintimacy":
	names = ['Very-intimate', 'Intimate', 'Somewhat-intimate', 'Not-very-intimate', 'Not-intimate', 'Not-intimate-at-all']
	elif self.config.type == "sexyn":
	names = ['not sexism', 'sexism']
	elif self.config.type == "intentyn":
	names = ['not intentional', 'intentional']
	elif self.config.type == "offensiveyn":
	names = ['not offensive', 'offensive']
	elif self.config.type == "hasbiasedimplication":
	names = ['not biased', 'biased']
	elif self.config.type == "trofi":
	names = ['metaphor', 'non-metaphor']
	elif self.config.type == "sentitreebank":
	names = ['positive', 'negative']
	elif self.config.type == "sarc":
	names = ['sarcastic', 'literal']
	elif self.config.type == "stanfordpoliteness":
	names = ['polite', 'impolite']
	elif self.config.type == "sarcasmghosh":
	names = ['sarcastic', 'literal']
	elif self.config.type == "dailydialog":
	names = ['noemotion', 'anger', 'disgust', 'fear', 'happiness', 'sadness', 'surprise']
	elif self.config.type == "shortromance":
	names = ['romantic', 'literal']
	elif self.config.type == "crowdflower":
	names = ['empty', 'sadness', 'enthusiasm', 'neutral', 'worry', 'love', 'fun', 'hate', 'happiness', 'relief', 'boredom', 'surprise', 'anger']
	elif self.config.type == "vua":
	names = ['metaphor', 'non-metaphor']
	elif self.config.type == "shorthumor":
	names = ['humorous', 'literal']
	elif self.config.type == "shortjokekaggle":
	names = ['humorous', 'literal']
	elif self.config.type == "hateoffensive":
	names = ['hate', 'offensive', 'neither']
	elif self.config.type == "emobank#valence":
	names = ['valence(positive)']
	elif self.config.type == "emobank#arousal":
	names = ['arousal(excited)']
	elif self.config.type == "emobank#dominance":
	names = ['dominance(being_in_control)']
	elif self.config.type == "hayati_politeness":
	names = ['impolite', 'polite']
	elif self.config.type == "jigsaw#toxic":
	names = ['not toxic', 'toxic']
	elif self.config.type == "jigsaw#severe_toxic":
	names = ['not severe toxic', 'severe toxic']
	elif self.config.type == "jigsaw#obscene":
	names = ['not obscene', 'obscene']
	elif self.config.type == "jigsaw#threat":
	names = ['not threat', 'threat']
	elif self.config.type == "jigsaw#insult":
	names = ['not insult', 'insult']
	elif self.config.type == "jigsaw#identity_hate":
	names = ['not identity hate', 'identity hate']
	elif self.config.type == "standup-comedy":
	names = ['not funny', 'funny']
	elif self.config.type == "complaints":
	names = ['not complaint', 'complaint']
	elif self.config.type == "hypo-l":
	names = ['not hyperbole', 'hyperbole']
	elif self.config.type == "bragging#brag_action":
	names = ['not action bragging', 'action bragging']
	elif self.config.type == "bragging#brag_feeling":
	names = ['not feeling bragging', 'feeling bragging']
	elif self.config.type == "bragging#brag_achievement":
	names = ['not achievement bragging', 'achievement bragging']
	elif self.config.type == "bragging#brag_possession":
	names = ['not possession bragging', 'possession bragging']
	elif self.config.type == "bragging#brag_trait":
	names = ['not trait bragging', 'trait bragging']
	elif self.config.type == "bragging#brag_affiliation":
	names = ['not affiliation bragging', 'affiliation bragging']
	elif self.config.type == "contextual-abuse#IdentityDirectedAbuse":
	names = ['not identity directed abuse', 'identity directed abuse']
	elif self.config.type == "contextual-abuse#AffiliationDirectedAbuse":
	names = ['not affiliation directed abuse', 'affiliation directed abuse']
	elif self.config.type == "contextual-abuse#PersonDirectedAbuse":
	names = ['not person directed abuse', 'person directed abuse']
	elif self.config.type == "contextual-abuse#CounterSpeech":
	names = ['not counter speech', 'counter speech']
	elif self.config.type == "hahackathon#is_humor":
	names = ['not humor', 'humor']
	elif self.config.type == "hahackathon#humor_rating":
	names = ['humor rating']
	elif self.config.type == "hahackathon#offense_rating":
	names = ['offense rating']
	elif self.config.type == "check_worthiness":
	names = ['not check-worthy', 'check-worthy']
	elif self.config.type == "rumor#rumor_tf":
	names = ['not rumor tf', 'rumor tf']
	elif self.config.type == "rumor#rumor_bool":
	names = ['not rumor', 'rumor']
	elif self.config.type == "two-to-lie#deception":
	names = ['not deception', 'deception']
	elif self.config.type == "two-to-lie#sender_truth":
	names = ['lie', 'truth']
	elif self.config.type == "two-to-lie#receiver_truth":
	names = ['lie', 'truth']
	elif self.config.type == "deceitful-reviews#true_rumor":
	names = ['fake review', 'true review']
	elif self.config.type == "deceitful-reviews#positive":
	names = ['negative', 'positive']
	elif self.config.type == "empathy#empathy":
	names = ['empathy']
	elif self.config.type == "empathy#distress":
	names = ['distress']
	elif self.config.type == "empathy#empathy_bin":
	names = ['not empathy', 'empathy']
	elif self.config.type == "empathy#distress_bin":
	names = ['not distress', 'distress bin']
	elif self.config.type == "implicit-hate#explicit_hate":
	names = ['not explicit hate', 'explicit hate']
	elif self.config.type == "implicit-hate#implicit_hate":
	names = ['not implicit hate', 'implicit hate']
	elif self.config.type == "implicit-hate#threatening_hate":
	names = ['not threatening hate', 'threatening hate']
	elif self.config.type == "implicit-hate#irony_hate":
	names = ['not irony hate', 'irony hate']
	elif self.config.type == "implicit-hate#other_hate":
	names = ['not other hate', 'other hate']
	elif self.config.type == "implicit-hate#incitement_hate":
	names = ['not incitement hate', 'incitement hate']
	elif self.config.type == "implicit-hate#inferiority_hate":
	names = ['not inferiority hate', 'inferiority hate']
	elif self.config.type == "implicit-hate#stereotypical_hate":
	names = ['not stereotypical hate', 'stereotypical hate']
	elif self.config.type == "implicit-hate#white_grievance_hate":
	names = ['not white grievance hate', 'white grievance hate']
	elif self.config.type == "waseem_and_hovy#sexism":
	names = ['not sexism', 'sexism']
	elif self.config.type == "waseem_and_hovy#racism":
	names = ['not racism', 'racism']
	elif self.config.type == "humor-pairs":
	names = ['the first sentence is funnier', 'the second sentence is funnier']
	elif self.config.type == "neutralizing-bias-pairs":
	names = ['the first sentence is biased', 'the second sentence is biased']
	elif self.config.type == "same-side-pairs":
	names = ['not same side', 'same side']
	elif self.config.type == "talkdown-pairs":
	names = ['not condescension', 'condescension']
	elif self.config.type == "tweet_sentiment":
	names = ["negative", "neutral", "positive"]
	elif self.config.type == "tweet_offensive":
	names = ["not offensive", "offensive"]
	elif self.config.type == "tweet_irony":
	names = ["not irony", "irony"]
	elif self.config.type == "tweet_hate":
	names = ["not hate", "hate"]
	elif self.config.type == "tweet_emoji":
	names = [
	"❤",
	"😍",
	"😂",
	"💕",
	"🔥",
	"😊",
	"😎",
	"✨",
	"💙",
	"😘",
	"📷",
	"🇺🇸",
	"☀",
	"💜",
	"😉",
	"💯",
	"😁",
	"🎄",
	"📸",
	"😜",
	]

	elif self.config.type == "tweet_emotion":
	names = ["anger", "joy", "optimism", "sadness"]
	elif self.config.type == "emotion-span":
	names = ['cause']
	label_type = datasets.Sequence(feature={n:datasets.Value(dtype='string', id=None) for n in names})
	print(label_type)
	elif self.config.type == "propaganda-span":
	names = ['propaganda']
	label_type = datasets.Sequence(feature={n:datasets.Value(dtype='string', id=None) for n in names})
	elif self.config.type == "toxic-span":
	names = ['toxic']
	label_type = datasets.Sequence(feature={n:datasets.Value(dtype='string', id=None) for n in names})

	if self.config.type[-4:]=='span':
	label_type = label_type#datasets.Sequence(feature={n:datasets.Value(dtype='string') for n in names})
	elif len(names) > 1:
	label_type = datasets.features.ClassLabel(names=names)
	else:
	label_type = datasets.Value("float32")


	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{"text": datasets.Value("string"),
	"label": label_type}
	),
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	my_urls = _URLs[self.config.type]
	data_dir = dl_manager.download_and_extract(my_urls)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"text_path": data_dir["train_text"], "labels_path": data_dir["train_labels"]},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"text_path": data_dir["test_text"], "labels_path": data_dir["test_labels"]},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"text_path": data_dir["val_text"], "labels_path": data_dir["val_labels"]},
	),
	datasets.SplitGenerator(
	name=datasets.NamedSplit('sockette'),
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"text_path": data_dir["sockette_text"], "labels_path": data_dir["sockette_labels"]},
	),
	]

	def _generate_examples(self, text_path, labels_path):
	"""Yields examples."""

	with open(text_path, encoding="utf-8") as f:
	texts = f.readlines()
	print(len(texts))
	with open(labels_path, encoding="utf-8") as f:
	labels = f.readlines()
	print(len(labels))

	for i, text in enumerate(texts):
	yield i, {"text": text.strip(), "label": labels[i].strip() if self.config.type[-4:]!='span' else eval(labels[i])}