prachuryyaIITG/SampurNER_Assamese_MuRIL

MuRIL is fine-tuned on Assamese SampurNER dataset for Fine-grained Named Entity Recognition. It is created using the EaMaTa framework, utilizing the Few-NERD dataset.

Read the paper: SampurNER in AAAI-2026

SampurNER Dataset: datasets/prachuryyaIITG/SampurNER

The tagset of Few-NERD is a fine-grained tagset. The fine to coarse level mapping of the tags are as follows:

• Location : GPE, Body of Water, Island, Mountain, Park, Road/Transit, Other
• Person : Actor, Artist/Author, Athlete, Director, Politician, Scholar, Soldier, Other
• ORG : Company, Education, Government, Media, Political Party, Religion, Sports League, Show Organization, Other
• Building : Airport, Hospital, Hotel, Library, Restaurant, Sports Facility, Theater, Other
• Art : Music, Film, Written Art, Broadcast, Painting, Other
• Product : Airplane, Car, Food, Game, Ship, Software, Train, Weapon, Other
• Event : Attack, Election, Natural Disaster, Protest, Sports Event, Other
• Misc : Astronomy, Award, Biology, Chemistry, Currency, Disease, Educational Degree, God, Language, Law, Living Thing, Medical

Model performance:

Precision: 65.54
Recall: 67.73
F1: 66.26

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{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{ding-etal-2021-nerd,
    title = "Few-{NERD}: A Few-shot Named Entity Recognition Dataset",
    author = "Ding, Ning and Xu, Guangwei and Chen, Yulin and Wang, Xiaobin and Han, Xu and Xie, Pengjun and Zheng, Haitao and Liu, Zhiyuan",
    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = aug,
    year = "2021",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.acl-long.248",
    doi = "10.18653/v1/2021.acl-long.248",
    pages = "3198--3213",
}