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

arXiv: https://arxiv.org/abs/2204.06029 · HTML: https://mlsanity.com/paper/2204.06029/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 33 references that are in the map.
- First posted on arXiv 12 Apr 2022.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [How multilingual is Multilingual BERT?](https://mlsanity.com/paper/1906.01502/) (2019)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [A Survey on Recent Advances in Named Entity Recognition from Deep Learning models](https://mlsanity.com/paper/1910.11470/) (2019)
- [L3Cube-MahaCorpus and MahaBERT: Marathi Monolingual Corpus, Marathi BERT Language Models, and Resources](https://mlsanity.com/paper/2202.01159/) (2022)
- [L3CubeMahaSent: A Marathi Tweet-based Sentiment Analysis Dataset](https://mlsanity.com/paper/2103.11408/) (2021)
- [Deep Learning for Hindi Text Classification: A Comparison](https://mlsanity.com/paper/2001.10340/) (2020)
- [Experimental Evaluation of Deep Learning models for Marathi Text Classification](https://mlsanity.com/paper/2101.04899/) (2021)
- [Hate and Offensive Speech Detection in Hindi and Marathi](https://mlsanity.com/paper/2110.12200/) (2021)

## Built on by (most cited first, 21 of 21)

- [L3Cube-HindBERT and DevBERT: Pre-Trained BERT Transformer models for Devanagari based Hindi and Marathi Languages](https://mlsanity.com/paper/2211.11418/) (2022)
- [L3Cube-MahaNLP: Marathi Natural Language Processing Datasets, Models, and Library](https://mlsanity.com/paper/2205.14728/) (2022)
- [Naamapadam: A Large-Scale Named Entity Annotated Data for Indic Languages](https://mlsanity.com/paper/2212.10168/) (2022)
- [Sebastian, Basti, Wastl?! Recognizing Named Entities in Bavarian Dialectal Data](https://mlsanity.com/paper/2403.12749/) (2024)
- [L3Cube-MahaSent-MD: A Multi-domain Marathi Sentiment Analysis Dataset and Transformer Models](https://mlsanity.com/paper/2306.13888/) (2023)
- [Breaking Language Barriers: A Question Answering Dataset for Hindi and Marathi](https://mlsanity.com/paper/2308.09862/) (2023)
- [Can LLM Substitute Human Labeling? A Case Study of Fine-grained Chinese Address Entity Recognition Dataset for UAV Delivery](https://mlsanity.com/paper/2403.06097/) (2024)
- [mahaNLP: A Marathi Natural Language Processing Library](https://mlsanity.com/paper/2311.02579/) (2023)
- [My Boli: Code-mixed Marathi-English Corpora, Pretrained Language Models and Evaluation Benchmarks](https://mlsanity.com/paper/2306.14030/) (2023)
- [Analysing Cross-Lingual Transfer in Low-Resourced African Named Entity Recognition](https://mlsanity.com/paper/2309.05311/) (2023)
- [L3Cube-MahaSocialNER: A Social Media based Marathi NER Dataset and BERT models](https://mlsanity.com/paper/2401.00170/) (2023)
- [MahaSQuAD: Bridging Linguistic Divides in Marathi Question-Answering](https://mlsanity.com/paper/2404.13364/) (2024)
- [OpenNER 1.0: Standardized Open-Access Named Entity Recognition Datasets in 50+ Languages](https://mlsanity.com/paper/2412.09587/) (2024)
- [TriNER: A Series of Named Entity Recognition Models For Hindi, Bengali & Marathi](https://mlsanity.com/paper/2502.04245/) (2025)
- [Enhancing Low Resource NER Using Assisting Language And Transfer Learning](https://mlsanity.com/paper/2306.06477/) (2023)
- [On Significance of Subword tokenization for Low Resource and Efficient Named Entity Recognition: A case study in Marathi](https://mlsanity.com/paper/2312.01306/) (2023)
- [AWED-PIPER: Agents, Web Applications & Expert Detectors for Personally Identifiable Information Protection & Fine-grained Named Entity Recognition across 36 languages for 6.6 Billion Speakers](https://mlsanity.com/paper/2601.10161/) (2026)
- [BhashaSetu: Cross-Lingual Knowledge Transfer from High-Resource to Extreme Low-Resource Languages](https://mlsanity.com/paper/2602.05599/) (2026)
- [L3Cube-MahaPOS: A Marathi Part-of-Speech Tagging Dataset and BERT Models](https://mlsanity.com/paper/2606.24825/) (2026)
- [BERT-based Models vs. Large Language Models for Low-Resource Named Entity Recognition: A Comparative Study on Marathi](https://mlsanity.com/paper/2607.23344/) (2026)
- [Named Entity Recognition using Sliding Window Approach](https://mlsanity.com/paper/2609.29682/) (2026)

## Abstract (arXiv)

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 .