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Social media platforms are used by a large number of people prominently toexpress their thoughts and opinions. However, these platforms have contributedto a substantial amount of hateful and abusive content as well. Therefore, itis important to curb the spread of hate speech on these platforms. In India,Marathi is one of the most popular languages used by a wide audience. In thiswork, we present L3Cube-MahaHate, the first major Hate Speech Dataset inMarathi. The dataset is curated from Twitter, annotated manually. Our datasetconsists of over 25000 distinct tweets labeled into four major classes i.ehate, offensive, profane, and not. We present the approaches used forcollecting and annotating the data and the challenges faced during the process.Finally, we present baseline classification results using deep learning modelsbased on CNN, LSTM, and Transformers. We explore mono-lingual and multi-lingualvariants of BERT like MahaBERT, IndicBERT, mBERT, and xlm-RoBERTa and show thatmono-lingual models perform better than their multi-lingual counterparts. TheMahaBERT model provides the best results on L3Cube-MahaHate Corpus. The dataand models are available at https://github.com/l3cube-pune/MarathiNLP .

L3Cube-MahaHate: A Tweet-based Marathi Hate Speech Detection Dataset and BERT models