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Named Entity Recognition (NER) is a basic NLP task and finds majorapplications in conversational and search systems. It helps us identify keyentities in a sentence used for the downstream application. NER or similar slotfilling systems for popular languages have been heavily used in commercialapplications. In this work, we focus on Marathi, an Indian language, spokenprominently by the people of Maharashtra state. Marathi is a low resourcelanguage and still lacks useful NER resources. We present L3Cube-MahaNER, thefirst major gold standard named entity recognition dataset in Marathi. We alsodescribe the manual annotation guidelines followed during the process. In theend, we benchmark the dataset on different CNN, LSTM, and Transformer basedmodels like mBERT, XLM-RoBERTa, IndicBERT, MahaBERT, etc. The MahaBERT providesthe best performance among all the models. The data and models are available athttps://github.com/l3cube-pune/MarathiNLP .

L3Cube-MahaNER: A Marathi Named Entity Recognition Dataset and BERT models