{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/hatebert-retraining-bert-for-abusive-language","title":"HateBERT: Retraining BERT for Abusive Language Detection in English","arxiv_id":"2010.12472","date":"2020-10-23","proceeding":"ACL (WOAH) 2021 8","authors":["Tommaso Caselli","Valerio Basile","Jelena Mitrović","Michael Granitzer"],"abstract":"In this paper, 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 collected and made available to the public. We present the results of a detailed comparison between a general pre-trained language model and the abuse-inclined version obtained by retraining with posts from the banned communities 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 generic pre-trained language model and its corresponding abusive language-inclined counterpart across the datasets, indicating that portability is affected by compatibility of the annotated phenomena.","url_abs":"https://arxiv.org/abs/2010.12472v2","url_pdf":"https://arxiv.org/pdf/2010.12472v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"hatebert-retraining-bert-for-abusive-language","repo_url":"https://github.com/tommasoc80/HateBERT","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"abusive-language","task_name":"Abusive Language"},{"task_slug":"hate-speech-detection","task_name":"Hate Speech Detection"},{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"}],"methods":[{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"attention-dropout","method_name":"Attention Dropout"},{"method_slug":"bert","method_name":"BERT"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"linear-warmup-with-linear-decay","method_name":"Linear Warmup With Linear Decay"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"weight-decay","method_name":"Weight Decay"},{"method_slug":"wordpiece","method_name":"WordPiece"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/hate-speech-detection-on-abuseval","task":"Hate Speech Detection","dataset":"AbusEval","model":"HateBERT","rank_in_archive_order":1,"of":2,"metrics":{"Macro F1":"0.742"},"uses_additional_data":false},{"leaderboard":"/sota/hate-speech-detection-on-abuseval","task":"Hate Speech Detection","dataset":"AbusEval","model":"BERT","rank_in_archive_order":2,"of":2,"metrics":{"Macro F1":"0.724"},"uses_additional_data":false},{"leaderboard":"/sota/hate-speech-detection-on-hateval","task":"Hate Speech Detection","dataset":"HatEval","model":"HateBERT","rank_in_archive_order":1,"of":2,"metrics":{"Macro F1":"0.494"},"uses_additional_data":false},{"leaderboard":"/sota/hate-speech-detection-on-hateval","task":"Hate Speech Detection","dataset":"HatEval","model":"BERT","rank_in_archive_order":2,"of":2,"metrics":{"Macro F1":"0.48"},"uses_additional_data":false},{"leaderboard":"/sota/hate-speech-detection-on-offenseval-2019","task":"Hate Speech Detection","dataset":"OffensEval 2019","model":"HateBERT","rank_in_archive_order":1,"of":2,"metrics":{"Macro F1":"0.805"},"uses_additional_data":false},{"leaderboard":"/sota/hate-speech-detection-on-offenseval-2019","task":"Hate Speech Detection","dataset":"OffensEval 2019","model":"BERT","rank_in_archive_order":2,"of":2,"metrics":{"Macro F1":"0.803"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2010.12472","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}