  Named entity recognition (NER) is the process of recognising and classifying
important information (entities) in text. Proper nouns, such as a person's
name, an organization's name, or a location's name, are examples of entities.
The NER is one of the important modules in applications like human resources,
customer support, search engines, content classification, and academia. In this
work, we consider NER for low-resource Indian languages like Hindi and Marathi.
The transformer-based models have been widely used for NER tasks. We consider
different variations of BERT like base-BERT, RoBERTa, and AlBERT and benchmark
them on publicly available Hindi and Marathi NER datasets. We provide an
exhaustive comparison of different monolingual and multilingual
transformer-based models and establish simple baselines currently missing in
the literature. We show that the monolingual MahaRoBERTa model performs the
best for Marathi NER whereas the multilingual XLM-RoBERTa performs the best for
Hindi NER. We also perform cross-language evaluation and present mixed
observations.
