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"""The SOCKET Datasets""" |
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import datasets |
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_CITATION = """ |
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@misc{choi2023llms, |
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title={Do LLMs Understand Social Knowledge? Evaluating the Sociability of Large Language Models with SocKET Benchmark}, |
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author={Minje Choi and Jiaxin Pei and Sagar Kumar and Chang Shu and David Jurgens}, |
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year={2023}, |
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eprint={2305.14938}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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""" |
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_DESCRIPTION = """\ |
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A unified evaluation benchmark dataset for evaludating socialbility of NLP models. |
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""" |
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_HOMEPAGE = "TBD" |
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_LICENSE = "" |
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URL = "SOCKET_DATA/" |
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URL = "https://huggingface.co/datasets/Blablablab/SOCKET/tree/main/SOCKET_DATA/" |
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TASK_DICT = { |
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'humor_sarcasm': [ |
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'hahackathon#humor_rating', |
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'humor-pairs', |
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'sarc', |
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'tweet_irony', |
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'hahackathon#is_humor', |
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], |
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'offensive': [ |
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'contextual-abuse#IdentityDirectedAbuse', |
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'contextual-abuse#PersonDirectedAbuse', |
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'hahackathon#offense_rating', |
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'hasbiasedimplication', |
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'hateoffensive', |
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'implicit-hate#explicit_hate', |
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'implicit-hate#implicit_hate', |
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'implicit-hate#incitement_hate', |
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'implicit-hate#inferiority_hate', |
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'implicit-hate#stereotypical_hate', |
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'implicit-hate#threatening_hate', |
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'implicit-hate#white_grievance_hate', |
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'intentyn', |
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'jigsaw#severe_toxic', |
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'jigsaw#identity_hate', |
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'jigsaw#threat', |
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'jigsaw#obscene', |
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'jigsaw#insult', |
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'jigsaw#toxic', |
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'offensiveyn', |
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'sexyn', |
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'talkdown-pairs', |
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'toxic-span', |
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'tweet_offensive' |
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], |
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'sentiment_emotion': [ |
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'crowdflower', |
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'dailydialog', |
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'emobank#arousal', |
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'emobank#dominance', |
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'emobank#valence', |
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'emotion-span', |
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'empathy#distress', |
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'empathy#distress_bin', |
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'same-side-pairs', |
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'sentitreebank', |
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'tweet_emoji', |
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'tweet_emotion', |
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'tweet_sentiment' |
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], |
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'social_factors': [ |
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'complaints', |
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'empathy#empathy', |
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'empathy#empathy_bin', |
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'hayati_politeness', |
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'questionintimacy', |
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'stanfordpoliteness' |
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], |
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'trustworthy': [ |
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'bragging#brag_achievement', |
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'bragging#brag_action', |
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'bragging#brag_possession', |
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'bragging#brag_trait', |
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'hypo-l', |
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'neutralizing-bias-pairs', |
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'propaganda-span', |
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'rumor#rumor_bool', |
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'two-to-lie#receiver_truth', |
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'two-to-lie#sender_truth', |
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] |
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} |
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task2category = {} |
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for category, tasks in TASK_DICT.items(): |
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for task in tasks: |
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task2category[task] = category |
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TASK_NAMES = [] |
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for tasks in TASK_DICT.values(): |
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TASK_NAMES.extend(tasks) |
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TASK_NAMES = sorted(TASK_NAMES) |
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print(len(TASK_NAMES)) |
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_URLs = {} |
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for task in TASK_NAMES: |
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_URLs[task] = {} |
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for s in ['train', 'test', 'val']: |
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for t in ['text', 'labels']: |
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task_url = '%s%s/%s_%s.txt'%(URL,task,s,t) |
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task_url = task_url.replace('#','%23') |
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_URLs[task][s + '_' + t] = task_url |
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class SOCKETConfig(datasets.BuilderConfig): |
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def __init__(self, *args, type=None, sub_type=None, **kwargs): |
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super().__init__( |
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*args, |
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name=f"{type}", |
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**kwargs, |
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) |
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self.type = type |
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self.sub_type = sub_type |
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class SOCKET(datasets.GeneratorBasedBuilder): |
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"""SOCKET Dataset.""" |
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BUILDER_CONFIGS = [ |
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SOCKETConfig( |
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type=key, |
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sub_type=None, |
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version=datasets.Version("1.1.0"), |
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description=f"This part of my dataset covers {key} part of SocialEval Dataset.", |
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) |
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for key in list(TASK_NAMES) |
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] |
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def _info(self): |
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if self.config.type == "questionintimacy": |
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names = ['Very-intimate', 'Intimate', 'Somewhat-intimate', 'Not-very-intimate', 'Not-intimate', 'Not-intimate-at-all'] |
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elif self.config.type == "sexyn": |
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names = ['not sexism', 'sexism'] |
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elif self.config.type == "intentyn": |
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names = ['not intentional', 'intentional'] |
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elif self.config.type == "offensiveyn": |
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names = ['not offensive', 'offensive'] |
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elif self.config.type == "hasbiasedimplication": |
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names = ['not biased', 'biased'] |
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elif self.config.type == "trofi": |
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names = ['metaphor', 'non-metaphor'] |
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elif self.config.type == "sentitreebank": |
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names = ['positive', 'negative'] |
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elif self.config.type == "sarc": |
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names = ['sarcastic', 'literal'] |
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elif self.config.type == "stanfordpoliteness": |
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names = ['polite', 'impolite'] |
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elif self.config.type == "sarcasmghosh": |
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names = ['sarcastic', 'literal'] |
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elif self.config.type == "dailydialog": |
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names = ['noemotion', 'anger', 'disgust', 'fear', 'happiness', 'sadness', 'surprise'] |
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elif self.config.type == "shortromance": |
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names = ['romantic', 'literal'] |
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elif self.config.type == "crowdflower": |
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names = ['empty', 'sadness', 'enthusiasm', 'neutral', 'worry', 'love', 'fun', 'hate', 'happiness', 'relief', 'boredom', 'surprise', 'anger'] |
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elif self.config.type == "vua": |
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names = ['metaphor', 'non-metaphor'] |
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elif self.config.type == "shorthumor": |
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names = ['humorous', 'literal'] |
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elif self.config.type == "shortjokekaggle": |
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names = ['humorous', 'literal'] |
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elif self.config.type == "hateoffensive": |
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names = ['hate', 'offensive', 'neither'] |
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elif self.config.type == "emobank#valence": |
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names = ['valence(positive)'] |
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elif self.config.type == "emobank#arousal": |
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names = ['arousal(excited)'] |
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elif self.config.type == "emobank#dominance": |
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names = ['dominance(being_in_control)'] |
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elif self.config.type == "hayati_politeness": |
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names = ['impolite', 'polite'] |
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elif self.config.type == "jigsaw#toxic": |
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names = ['not toxic', 'toxic'] |
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elif self.config.type == "jigsaw#severe_toxic": |
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names = ['not severe toxic', 'severe toxic'] |
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elif self.config.type == "jigsaw#obscene": |
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names = ['not obscene', 'obscene'] |
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elif self.config.type == "jigsaw#threat": |
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names = ['not threat', 'threat'] |
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elif self.config.type == "jigsaw#insult": |
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names = ['not insult', 'insult'] |
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elif self.config.type == "jigsaw#identity_hate": |
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names = ['not identity hate', 'identity hate'] |
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elif self.config.type == "standup-comedy": |
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names = ['not funny', 'funny'] |
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elif self.config.type == "complaints": |
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names = ['not complaint', 'complaint'] |
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elif self.config.type == "hypo-l": |
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names = ['not hyperbole', 'hyperbole'] |
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elif self.config.type == "bragging#brag_action": |
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names = ['not action bragging', 'action bragging'] |
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elif self.config.type == "bragging#brag_feeling": |
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names = ['not feeling bragging', 'feeling bragging'] |
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elif self.config.type == "bragging#brag_achievement": |
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names = ['not achievement bragging', 'achievement bragging'] |
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elif self.config.type == "bragging#brag_possession": |
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names = ['not possession bragging', 'possession bragging'] |
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elif self.config.type == "bragging#brag_trait": |
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names = ['not trait bragging', 'trait bragging'] |
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elif self.config.type == "bragging#brag_affiliation": |
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names = ['not affiliation bragging', 'affiliation bragging'] |
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elif self.config.type == "contextual-abuse#IdentityDirectedAbuse": |
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names = ['not identity directed abuse', 'identity directed abuse'] |
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elif self.config.type == "contextual-abuse#AffiliationDirectedAbuse": |
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names = ['not affiliation directed abuse', 'affiliation directed abuse'] |
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elif self.config.type == "contextual-abuse#PersonDirectedAbuse": |
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names = ['not person directed abuse', 'person directed abuse'] |
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elif self.config.type == "contextual-abuse#CounterSpeech": |
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names = ['not counter speech', 'counter speech'] |
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elif self.config.type == "hahackathon#is_humor": |
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names = ['not humor', 'humor'] |
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elif self.config.type == "hahackathon#humor_rating": |
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names = ['humor rating'] |
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elif self.config.type == "hahackathon#offense_rating": |
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names = ['offense rating'] |
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elif self.config.type == "check_worthiness": |
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names = ['not check-worthy', 'check-worthy'] |
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elif self.config.type == "rumor#rumor_tf": |
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names = ['not rumor tf', 'rumor tf'] |
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elif self.config.type == "rumor#rumor_bool": |
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names = ['not rumor', 'rumor'] |
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elif self.config.type == "two-to-lie#deception": |
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names = ['not deception', 'deception'] |
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elif self.config.type == "two-to-lie#sender_truth": |
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names = ['lie', 'truth'] |
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elif self.config.type == "two-to-lie#receiver_truth": |
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names = ['lie', 'truth'] |
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elif self.config.type == "deceitful-reviews#true_rumor": |
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names = ['fake review', 'true review'] |
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elif self.config.type == "deceitful-reviews#positive": |
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names = ['negative', 'positive'] |
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elif self.config.type == "empathy#empathy": |
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names = ['empathy'] |
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elif self.config.type == "empathy#distress": |
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names = ['distress'] |
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elif self.config.type == "empathy#empathy_bin": |
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names = ['not empathy', 'empathy'] |
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elif self.config.type == "empathy#distress_bin": |
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names = ['not distress', 'distress bin'] |
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elif self.config.type == "implicit-hate#explicit_hate": |
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names = ['not explicit hate', 'explicit hate'] |
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elif self.config.type == "implicit-hate#implicit_hate": |
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names = ['not implicit hate', 'implicit hate'] |
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elif self.config.type == "implicit-hate#threatening_hate": |
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names = ['not threatening hate', 'threatening hate'] |
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elif self.config.type == "implicit-hate#irony_hate": |
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names = ['not irony hate', 'irony hate'] |
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elif self.config.type == "implicit-hate#other_hate": |
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names = ['not other hate', 'other hate'] |
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elif self.config.type == "implicit-hate#incitement_hate": |
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names = ['not incitement hate', 'incitement hate'] |
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elif self.config.type == "implicit-hate#inferiority_hate": |
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names = ['not inferiority hate', 'inferiority hate'] |
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elif self.config.type == "implicit-hate#stereotypical_hate": |
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names = ['not stereotypical hate', 'stereotypical hate'] |
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elif self.config.type == "implicit-hate#white_grievance_hate": |
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names = ['not white grievance hate', 'white grievance hate'] |
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elif self.config.type == "waseem_and_hovy#sexism": |
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names = ['not sexism', 'sexism'] |
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elif self.config.type == "waseem_and_hovy#racism": |
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names = ['not racism', 'racism'] |
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elif self.config.type == "humor-pairs": |
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names = ['the first sentence is funnier', 'the second sentence is funnier'] |
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elif self.config.type == "neutralizing-bias-pairs": |
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names = ['the first sentence is biased', 'the second sentence is biased'] |
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elif self.config.type == "same-side-pairs": |
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names = ['not same side', 'same side'] |
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elif self.config.type == "talkdown-pairs": |
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names = ['not condescension', 'condescension'] |
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elif self.config.type == "tweet_sentiment": |
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names = ["negative", "neutral", "positive"] |
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elif self.config.type == "tweet_offensive": |
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names = ["not offensive", "offensive"] |
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elif self.config.type == "tweet_irony": |
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names = ["not irony", "irony"] |
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elif self.config.type == "tweet_hate": |
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names = ["not hate", "hate"] |
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elif self.config.type == "tweet_emoji": |
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names = [ |
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"β€", |
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"π", |
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"π", |
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"π", |
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"π₯", |
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"π", |
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"π", |
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"β¨", |
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"π", |
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"π", |
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"π·", |
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"πΊπΈ", |
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"β", |
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"π", |
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"π", |
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"π―", |
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"π", |
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"π", |
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"πΈ", |
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"π", |
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] |
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elif self.config.type == "tweet_emotion": |
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names = ["anger", "joy", "optimism", "sadness"] |
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elif self.config.type == "emotion-span": |
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names = ['cause'] |
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label_type = datasets.Sequence(feature={n:datasets.Value(dtype='string', id=None) for n in names}) |
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print(label_type) |
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elif self.config.type == "propaganda-span": |
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names = ['propaganda'] |
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label_type = datasets.Sequence(feature={n:datasets.Value(dtype='string', id=None) for n in names}) |
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elif self.config.type == "toxic-span": |
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names = ['toxic'] |
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label_type = datasets.Sequence(feature={n:datasets.Value(dtype='string', id=None) for n in names}) |
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if self.config.type[-4:]=='span': |
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label_type = label_type |
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elif len(names) > 1: |
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label_type = datasets.features.ClassLabel(names=names) |
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else: |
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label_type = datasets.Value("float32") |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{"text": datasets.Value("string"), |
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"label": label_type} |
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), |
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supervised_keys=None, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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my_urls = _URLs[self.config.type] |
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data_dir = dl_manager.download_and_extract(my_urls) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={"text_path": data_dir["train_text"], "labels_path": data_dir["train_labels"]}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={"text_path": data_dir["test_text"], "labels_path": data_dir["test_labels"]}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={"text_path": data_dir["val_text"], "labels_path": data_dir["val_labels"]}, |
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), |
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] |
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def _generate_examples(self, text_path, labels_path): |
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"""Yields examples.""" |
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|
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with open(text_path, encoding="utf-8") as f: |
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texts = f.readlines() |
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print(len(texts)) |
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with open(labels_path, encoding="utf-8") as f: |
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labels = f.readlines() |
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print(len(labels)) |
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for i, text in enumerate(texts): |
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yield i, {"text": text.strip(), "label": labels[i].strip() if self.config.type[-4:]!='span' else eval(labels[i])} |