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 GitHub 1
翻译:本文介绍向德拉维迪安-科多米克斯-哈萨科2021:德拉维迪安语言(塔米尔-英语和马利亚拉姆-英语)提交的系统:在德拉维迪安语言(塔米尔-英语和马利亚拉姆-英语)中,仇恨言论和攻击性语言识别系统:这一任务旨在确定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来完成这一任务,这帮助我们在领导板上达到九级,在亚塔斯克B的泰米尔-英语数据集中,加权平均得分为0.61分。任务期限过后,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,这有助于我们达到0.67的加权平均分,即领导板的最高分。此外,我们使用事先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执行同一任务。我们的代码和模型可在GitHub 1查阅。