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We present, Naamapadam, the largest publicly available Named EntityRecognition (NER) dataset for the 11 major Indian languages from two languagefamilies. The dataset contains more than 400k sentences annotated with a totalof at least 100k entities from three standard entity categories (Person,Location, and, Organization) for 9 out of the 11 languages. The trainingdataset has been automatically created from the Samanantar parallel corpus byprojecting automatically tagged entities from an English sentence to thecorresponding Indian language translation. We also create manually annotatedtestsets for 9 languages. We demonstrate the utility of the obtained dataset onthe Naamapadam-test dataset. We also release IndicNER, a multilingual IndicBERTmodel fine-tuned on Naamapadam training set. IndicNER achieves an F1 score ofmore than $80$ for $7$ out of $9$ test languages. The dataset and models areavailable under open-source licences athttps://ai4bharat.iitm.ac.in/naamapadam.

Naamapadam: A Large-Scale Named Entity Annotated Data for Indic Languages