MultiCoNER2_Hindi_XLM

XLM-RoBERTa is fine-tuned on Hindi MultiCoNER2 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:

• Location (LOC) : Facility, OtherLOC, HumanSettlement, Station
• Creative Work (CW) : VisualWork, MusicalWork, WrittenWork, ArtWork, Software
• Group (GRP) : MusicalGRP, PublicCORP, PrivateCORP, AerospaceManufacturer, SportsGRP, CarManufacturer, ORG
• Person (PER) : Scientist, Artist, Athlete, Politician, Cleric, SportsManager, OtherPER
• Product (PROD) : Clothing, Vehicle, Food, Drink, OtherPROD
• Medical (MED) : Medication/Vaccine, MedicalProcedure, AnatomicalStructure, Symptom, Disease

Model performance:

Precision: 76.07 <br> Recall: 79.42 <br> F1: 77.71 <br>

Training Parameters:

Epochs: 6 <br> Optimizer: AdamW <br> Learning Rate: 5e-5 <br> Weight Decay: 0.01 <br> Batch Size: 64 <br>

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

Sample Usage of Agentic Tool

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="Jude Bellingham joined Real Madrid in 2023.",
    language="English"
)

print(result)

Citation

If you use this model, please cite the following papers:

@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}
}

@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{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}
}