  In our increasingly interconnected digital world, social media platforms have
emerged as powerful channels for the dissemination of hate speech and offensive
content. This work delves into the domain of hate speech detection, placing
specific emphasis on three low-resource Indian languages: Bengali, Assamese,
and Gujarati. The challenge is framed as a text classification task, aimed at
discerning whether a tweet contains offensive or non-offensive content.
Leveraging the HASOC 2023 datasets, we fine-tuned pre-trained BERT and SBERT
models to evaluate their effectiveness in identifying hate speech. Our findings
underscore the superiority of monolingual sentence-BERT models, particularly in
the Bengali language, where we achieved the highest ranking. However, the
performance in Assamese and Gujarati languages signifies ongoing opportunities
for enhancement. Our goal is to foster inclusive online spaces by countering
hate speech proliferation.
