# TriNER: A Series of Named Entity Recognition Models For Hindi, Bengali & Marathi

arXiv: https://arxiv.org/abs/2502.04245 · HTML: https://mlsanity.com/paper/2502.04245/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 20 references that are in the map.
- First posted on arXiv 6 Feb 2025.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [MuRIL: Multilingual Representations for Indian Languages](https://mlsanity.com/paper/2103.10730/) (2021)
- [Naamapadam: A Large-Scale Named Entity Annotated Data for Indic Languages](https://mlsanity.com/paper/2212.10168/) (2022)
- [L3Cube-MahaNER: A Marathi Named Entity Recognition Dataset and BERT models](https://mlsanity.com/paper/2204.06029/) (2022)
- [HiNER: A Large Hindi Named Entity Recognition Dataset](https://mlsanity.com/paper/2204.13743/) (2022)
- [Benchmarking Advanced Text Anonymisation Methods: A Comparative Study on Novel and Traditional Approaches](https://mlsanity.com/paper/2404.14465/) (2024)

## Built on by (most cited first, 1 of 1)

- [ANCHOLIK-NER: A Benchmark Dataset for Bangla Regional Named Entity Recognition](https://mlsanity.com/paper/2502.11198/) (2025)

## Abstract (arXiv)

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.