Transformers are the most eminent architectures used for a vast range of Natural Language Processing tasks. These models are pre-trained over a large text corpus and are meant to serve state-of-the-art results over tasks like text classification. In this work, we conduct a comparative study between monolingual and multilingual BERT models. We focus on the Marathi language and evaluate the models on the datasets for hate speech detection, sentiment analysis and simple text classification in Marathi. We use standard multilingual models such as mBERT, indicBERT and xlm-RoBERTa and compare with MahaBERT, MahaALBERT and MahaRoBERTa, the monolingual models for Marathi. We further show that Marathi monolingual models outperform the multilingual BERT variants on five different downstream fine-tuning experiments. We also evaluate sentence embeddings from these models by freezing the BERT encoder layers. We show that monolingual MahaBERT based models provide rich representations as compared to sentence embeddings from multi-lingual counterparts. However, we observe that these embeddings are not generic enough and do not work well on out of domain social media datasets. We consider two Marathi hate speech datasets L3Cube-MahaHate, HASOC-2021, a Marathi sentiment classification dataset L3Cube-MahaSent, and Marathi Headline, Articles classification datasets.

在本文中，我们使用 Marathi 语言的标准多语言模型和单语模型比较分析，通过五种不同的下游任务微调实验证明了单语 MahaBERT 模型的性能比多语言 BERT 变体更好。同时，我们还评估了来自这些模型的句子嵌入。

马拉地语中仇恨言论检测和文本分类的单语和多语BERT案例研究