Social media platforms are used by a large number of people prominently to express their thoughts and opinions. However, these platforms have contributed to a substantial amount of hateful and abusive content as well. Therefore, it is 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 this work, we present L3Cube-MahaHate, the first major Hate Speech Dataset in Marathi. The dataset is curated from Twitter, annotated manually. Our dataset consists of over 25000 distinct tweets labeled into four major classes i.e hate, offensive, profane, and not. We present the approaches used for collecting and annotating the data and the challenges faced during the process. Finally, we present baseline classification results using deep learning models based on CNN, LSTM, and Transformers. We explore mono-lingual and multi-lingual variants of BERT like MahaBERT, IndicBERT, mBERT, and xlm-RoBERTa and show that mono-lingual models perform better than their multi-lingual counterparts. The MahaBERT model provides the best results on L3Cube-MahaHate Corpus. The data and models are available at https://github.com/l3cube-pune/MarathiNLP .

该研究介绍了L3Cube-MahaHate，这是首个主要的马拉地语恶意言论数据集，收集自Twitter上的超过25000条不同推文，并基于卷积神经网络、LSTM和Transformers构建基线分类模型，结果表明单语言模型胜于多语言模型，其中MahaBERT模型在L3Cube-MahaHate语料库上表现最佳。

L3Cube-MahaHate: 基于推特的马拉提语仇恨言论检测数据集与BERT模型