Hate speech is considered to be one of the major issues currently plaguing online social media. Repeated and repetitive exposure to hate speech has been shown to create physiological effects on the target users. Thus, hate speech, in all its forms, should be addressed on these platforms in order to maintain good health. In this paper, we explored several Transformer based machine learning models for the detection of hate speech and offensive content in English and Indo-Aryan languages at FIRE 2021. We explore several models such as mBERT, XLMR-large, XLMR-base by team name "Super Mario". Our models came 2nd position in Code-Mixed Data set (Macro F1: 0.7107), 2nd position in Hindi two-class classification(Macro F1: 0.7797), 4th in English four-class category (Macro F1: 0.8006) and 12th in English two-class category (Macro F1: 0.6447).
翻译：仇恨言论被认为是目前困扰在线社交媒体的主要问题之一,反复和重复地揭露仇恨言论已经证明对目标用户产生生理影响,因此,为了保持健康,应在这些平台上处理各种形式的仇恨言论,以保持健康。在本文中,我们探讨了若干基于变换器的机器学习模式,以在FIRE 2021中检测英语和印阿利安语中的仇恨言论和冒犯性内容。我们探索了几种模式,如mBERT、XLMR(大型)、XLMR(以团队名称“超级马里奥”)为基地。我们的模型在代码混合数据集(Macro F1:0.7107)、印地语二类第二位(Macro F1:0.7797)、英文四类第四位(Macro F1:0.8006)和英文二类第十二位(Macro F1:0.6447)。
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