MuRIL is fine-tuned on Bodo CLASSER 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: 73.83
Recall: 76.37
F1: 75.08

Training Parameters:

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

Citation

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

@inproceedings{kaushik2025classer,
  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},
  year      = {2025},
  publisher = {Association for Computational Linguistics},
  note      = {Main conference paper}
}

@inproceedings{kaushik2026sampurner,
  title={SampurNER: Fine-grained Named Entity Recognition Dataset for 22 Indian Languages},
  author={Kaushik, Prachuryya and Anand, Ashish},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={40},
  year={2026}
}

@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}
}