India's rich cultural and linguistic diversity poses various challenges in the domain of Natural Language Processing (NLP), particularly in Named Entity Recognition (NER). NER is a NLP task that aims to identify and classify tokens into different entity groups like Person, Location, Organization, Number, etc. This makes NER very useful for downstream tasks like context-aware anonymization. This paper details our work to build a multilingual NER model for the three most spoken languages in India - Hindi, Bengali & Marathi. We train a custom transformer model and fine tune a few pretrained models, achieving an F1 Score of 92.11 for a total of 6 entity groups. Through this paper, we aim to introduce a single model to perform NER and significantly reduce the inconsistencies in entity groups and tag names, across the three languages.
View on arXiv@article{dhamaskar2025_2502.04245,
title={ TriNER: A Series of Named Entity Recognition Models For Hindi, Bengali & Marathi },
author={ Mohammed Amaan Dhamaskar and Rasika Ransing },
journal={arXiv preprint arXiv:2502.04245},
year={ 2025 }
}