🚀 HateBERT模型

HateBERT是一个用于英文文本分类的预训练模型，它专注于检测辱骂性语言、仇恨言论和冒犯性语言，能有效解决在英文文本中识别不良语言的问题，为构建健康的网络语言环境提供有力支持。

🚀 快速开始

HateBERT是一个英文预训练的BERT模型，它通过使用来自Reddit被封禁社区的100多万条帖子，对英文BERT基础无大小写模型进行进一步训练而得到。该模型由格罗宁根大学、都灵大学和帕绍大学合作开发。

如需了解详细信息，请查看在 WOAH 2021 会议上发表的论文。代码和微调后的模型可在 OSF 上获取。

📚 详细文档

作者信息

• Tommaso Caselli
• Valerio Basile
• Jelena Mitrovic
• Michael Granizter

BibTeX引用信息

@inproceedings{caselli-etal-2021-hatebert,
    \ttitle = "{H}ate{BERT}: Retraining {BERT} for Abusive Language Detection in {E}nglish",
    \tauthor = "Caselli, Tommaso  and
      Basile, Valerio  and
      Mitrovi{\'c}, Jelena  and
      Granitzer, Michael",
    \tbooktitle = "Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021)",
    \tmonth = aug,
    \tyear = "2021",
    \taddress = "Online",
    \tpublisher = "Association for Computational Linguistics",
    \tturl = "https://aclanthology.org/2021.woah-1.3",
    \tdoi = "10.18653/v1/2021.woah-1.3",
    \tpages = "17--25",
    \tabstract = "We introduce HateBERT, a re-trained BERT model for abusive language detection in English. The model was trained on RAL-E, a large-scale dataset of Reddit comments in English from communities banned for being offensive, abusive, or hateful that we have curated and made available to the public. We present the results of a detailed comparison between a general pre-trained language model and the retrained version on three English datasets for offensive, abusive language and hate speech detection tasks. In all datasets, HateBERT outperforms the corresponding general BERT model. We also discuss a battery of experiments comparing the portability of the fine-tuned models across the datasets, suggesting that portability is affected by compatibility of the annotated phenomena.",
}