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prachuryyaIITG/FiNERVINER_Bodo_IndicBERTv2
FiNERVINER_Bodo_IndicBERTv2 is a token classification model from prachuryyaIITG. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as mit.
IndicBERTv2 is fine-tuned on the Bodo FiNERVINER dataset for Fine-grained Named Entity Recognition.
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From the Hugging Face model README
IndicBERTv2 is fine-tuned on the Bodo FiNERVINER dataset for Fine-grained Named Entity Recognition.
The tagset of MultiCoNER2 is a fine-grained tagset. The fine to coarse level mapping of the tags are as follows:
Please read the FiNERVINER paper in LREC'26 proceedings
Precision: 63.85 <br> Recall: 67.57 <br> F1: 65.66 <br>
Epochs: 6 <br> Optimizer: AdamW <br> Learning Rate: 5e-5 <br> Weight Decay: 0.01 <br> Batch Size: 64 <br>
Prachuryya Kaushik <br> Prof. Ashish Anand
It is part of the AWED-PIPER ecosystem: Paper | Agent for FgNER | Web App for FgNER | Agent for PII Protection | Web App for PII Protection
The AWED-FiNER agentic tool can be used to interact with expert models trained using this framework. Below is an example:
pip install smolagents gradio_client
from tool import AWEDFiNERTool
tool = AWEDFiNERTool(
space_id="prachuryyaIITG/AWED-FiNER"
)
result = tool.forward(
text="अमिताभ बच्चनआ सासे मुंदांखा फावखुंगुर।",
language="Bodo"
)
print(result)
If you use this model, please cite the following papers:
@inproceedings{kaushik-etal-2026-finerviner,
title = {FiNERVINER: Fine-grained Named Entity Recognition for Vulnerable Languages of India's North Eastern Region},
author = {Kaushik, Prachuryya and Anand, Ashish},
booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference (LREC 2026)},
month = {May},
year = {2026},
pages = {7655--7667},
address = {Palma, Mallorca, Spain},
publisher = {European Language Resources Association (ELRA)},
doi = {10.63317/3rs5mcedzvss}
}
@misc{kaushik2026awedpiperagentswebapplications,
title={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},
author={Prachuryya Kaushik and Ashish Anand},
year={2026},
eprint={2601.10161},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2601.10161},
}
@inproceedings{kaushik-anand-2025-classer,
title = "{CLASSER}: Cross-lingual Annotation Projection enhancement through Script Similarity for Fine-grained Named Entity Recognition",
author = "Kaushik, Prachuryya and
Anand, Ashish",
booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
month = dec,
year = "2025",
address = "Mumbai, India",
publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
url = "https://aclanthology.org/2025.ijcnlp-long.94/",
pages = "1745--1760",
ISBN = "979-8-89176-298-5",
}
@inproceedings{kaushik2026sampurner,
title={SampurNER: Fine-Grained Named Entity Recognition Dataset for 22 Indian Languages},
volume={40},
url={https://ojs.aaai.org/index.php/AAAI/article/view/40405},
DOI={10.1609/aaai.v40i37.40405},
number={37},
journal={Proceedings of the AAAI Conference on Artificial Intelligence},
author={Kaushik, Prachuryya and Anand, Ashish},
year={2026},
month={Mar.},
pages={31410-31418}
}
@inproceedings{fetahu2023multiconer,
title={MultiCoNER v2: a Large Multilingual dataset for Fine-grained and Noisy Named Entity Recognition},
author={Fetahu, Besnik and Chen, Zhiyu and Kar, Sudipta and Rokhlenko, Oleg and Malmasi, Shervin},
booktitle={Findings of the Association for Computational Linguistics: EMNLP 2023},
pages={2027--2051},
year={2023}
}