cardiffnlp/twitter-roberta-base-hate-latest

该模型是对 cardiffnlp/twitter-roberta-base-2022-154m 进行二进制仇恨言论分类的微调版本。使用了13个不同的英文仇恨言论数据集对模型进行了微调。

下面是所达到的指标

Dataset	Accuracy	Macro-F1	Weighted-F1
hatEval, SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter	0.5831	0.5646	0.548
ucberkeley-dlab/measuring-hate-speech	0.9273	0.9193	0.928
Detecting East Asian Prejudice on Social Media	0.9231	0.6623	0.9428
Call me sexist, but	0.9686	0.9203	0.9696
Predicting the Type and Target of Offensive Posts in Social Media	0.9164	0.6847	0.9098
HateXplain	0.8653	0.845	0.8662
Large Scale Crowdsourcing and Characterization of Twitter Abusive BehaviorLarge Scale Crowdsourcing and Characterization of Twitter Abusive Behavior	0.7801	0.7446	0.7614
Multilingual and Multi-Aspect Hate Speech Analysis	0.9944	0.4986	0.9972
Hate speech and offensive content identification in indo-european languages	0.8779	0.6904	0.8706
Are You a Racist or Am I Seeing Things?	0.921	0.8935	0.9216
Automated Hate Speech Detection	0.9423	0.9249	0.9429
Hate Towards the Political Opponent	0.8783	0.6595	0.8788
Hateful Symbols or Hateful People?	0.8187	0.7833	0.8323
Overall	0.8766	0.7531	0.8745

用法

通过pip安装tweetnlp。

pip install tweetnlp

在python中加载模型。

import tweetnlp
model = tweetnlp.Classifier("cardiffnlp/twitter-roberta-base-hate-latest")
model.predict('I love everybody :)')
>> {'label': 'NOT-HATE'}

基于的模型：

@misc{antypas2023robust,
      title={Robust Hate Speech Detection in Social Media: A Cross-Dataset Empirical Evaluation}, 
      author={Dimosthenis Antypas and Jose Camacho-Collados},
      year={2023},
      eprint={2307.01680},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

作者:

Cardiff NLP

数据集大小:

954.54 MB