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Automated offensive language detection is essential in combating the spreadof hate speech, particularly in social media. This paper describes our work onOffensive Language Identification in low resource Indic language Marathi. Theproblem is formulated as a text classification task to identify a tweet asoffensive or non-offensive. We evaluate different mono-lingual andmulti-lingual BERT models on this classification task, focusing on BERT modelspre-trained with social media datasets. We compare the performance of MuRIL,MahaTweetBERT, MahaTweetBERT-Hateful, and MahaBERT on the HASOC 2022 test set.We also explore external data augmentation from other existing Marathi hatespeech corpus HASOC 2021 and L3Cube-MahaHate. The MahaTweetBERT, a BERT model,pre-trained on Marathi tweets when fine-tuned on the combined dataset (HASOC2021 + HASOC 2022 + MahaHate), outperforms all models with an F1 score of 98.43on the HASOC 2022 test set. With this, we also provide a new state-of-the-artresult on HASOC 2022 / MOLD v2 test set.

A Twitter BERT Approach for Offensive Language Detection in Marathi