  Automated offensive language detection is essential in combating the spread
of hate speech, particularly in social media. This paper describes our work on
Offensive Language Identification in low resource Indic language Marathi. The
problem is formulated as a text classification task to identify a tweet as
offensive or non-offensive. We evaluate different mono-lingual and
multi-lingual BERT models on this classification task, focusing on BERT models
pre-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 hate
speech corpus HASOC 2021 and L3Cube-MahaHate. The MahaTweetBERT, a BERT model,
pre-trained on Marathi tweets when fine-tuned on the combined dataset (HASOC
2021 + HASOC 2022 + MahaHate), outperforms all models with an F1 score of 98.43
on the HASOC 2022 test set. With this, we also provide a new state-of-the-art
result on HASOC 2022 / MOLD v2 test set.
