This paper describes the system submitted to Dravidian-Codemix-HASOC2021: Hate Speech and Offensive Language Identification in Dravidian Languages (Tamil-English and Malayalam-English). This task aims to identify offensive content in code-mixed comments/posts in Dravidian Languages collected from social media. Our approach utilizes pooling the last layers of pretrained transformer multilingual BERT for this task which helped us achieve rank nine on the leaderboard with a weighted average score of 0.61 for the Tamil-English dataset in subtask B. After the task deadline, we sampled the dataset uniformly and used the MuRIL pretrained model, which helped us achieve a weighted average score of 0.67, the top score in the leaderboard. Furthermore, our approach to utilizing the pretrained models helps reuse our models for the same task with a different dataset. Our code and models are available in https://github.com/seanbenhur/tanglish-offensive-language-identification

翻译：本文介绍向德拉维迪安-科多米克斯-哈索克2021:德拉维迪安语言(泰米尔-英语和马来亚-英语)提交的系统:在德拉维迪安语言(泰米尔-英语和马来亚-英语)中,仇恨言论和攻击性语言识别系统;这一任务旨在识别从社交媒体收集的德拉维迪安语言代码混合评论/文章中的冒犯性内容。我们的方法是集中最后一层经过培训的变压器多语种BERT来完成这项任务,这帮助我们在领导板上取得九级的泰米尔-英语数据集的加权平均得分为0.61。任务完成后,我们统一地抽样了数据集,并使用了穆里尔预先培训的模式,帮助我们在领先板上获得0.67的加权平均分,这是最高分。此外,我们利用这些经过培训的模型的方法有助于用不同的数据集重新利用我们的模型来完成同样的任务。我们的代码和模型可在https://github.com/seanbenhur/tangrish-offerview-ypeal-wing-wage-degration-defation)。