  Named Entity Recognition (NER) is a basic NLP task and finds major
applications in conversational and search systems. It helps us identify key
entities in a sentence used for the downstream application. NER or similar slot
filling systems for popular languages have been heavily used in commercial
applications. In this work, we focus on Marathi, an Indian language, spoken
prominently by the people of Maharashtra state. Marathi is a low resource
language and still lacks useful NER resources. We present L3Cube-MahaNER, the
first major gold standard named entity recognition dataset in Marathi. We also
describe the manual annotation guidelines followed during the process. In the
end, we benchmark the dataset on different CNN, LSTM, and Transformer based
models like mBERT, XLM-RoBERTa, IndicBERT, MahaBERT, etc. The MahaBERT provides
the best performance among all the models. The data and models are available at
https://github.com/l3cube-pune/MarathiNLP .
