---
base_model:
- google/muril-large-cased
datasets:
- DFKI-SLT/few-nerd
language:
- en
license: mit
metrics:
- f1
- precision
- recall
pipeline_tag: other
tags:
- NER
- Named_Entity_Recognition
pretty_name: FewNERD English MuRIL
library_name: transformers
---

This model is an expert detector for Fine-grained Named Entity Recognition (FgNER) within the **AWED-FiNER** project. It is a fine-tuned version of `google/muril-large-cased` on the English [Few-NERD](https://huggingface.co/datasets/DFKI-SLT/few-nerd) dataset.

**AWED-FiNER** is presented in the paper: [AWED-FiNER: Agents, Web applications, and Expert Detectors for Fine-grained Named Entity Recognition across 36 Languages for 6.6 Billion Speakers](https://huggingface.co/papers/2601.10161).

Read the papers: [FewNERD in ACL-2021](https://aclanthology.org/2021.acl-long.248.pdf) , [SampurNER in AAAI-2026](https://github.com/PrachuryyaKaushik/SampurNER/blob/main/SampurNER_AAAI_extended.pdf)

The tagset of [Few-NERD](https://aclanthology.org/2021.acl-long.248.pdf) 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: 66.21 <br>
Recall: 69.98 <br>
**F1: 68.04** <br>

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

[**AWED-FiNER collection**](https://huggingface.co/collections/prachuryyaIITG/awed-finer) | [**Paper**](https://huggingface.co/papers/2601.10161) | [**Agentic Tool**](https://github.com/PrachuryyaKaushik/AWED-FiNER) | [**Interactive Demo**](https://huggingface.co/spaces/prachuryyaIITG/AWED-FiNER)

## 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:
```bash
pip install smolagents gradio_client
```
```python
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:

```bibtex
@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",
}

@misc{kaushik2026awedfiner,
  title        = {AWED-FiNER: Agents, Web Applications, and Expert Detectors for Fine-grained Named Entity Recognition across 36 Languages for 6.6 Billion Speakers},
  author       = {Kaushik, Prachuryya and Anand, Ashish},
  year         = {2026},
  note         = {arXiv preprint, submitted},
  archivePrefix= {arXiv},
  eprint       = {2601.10161},
  url          = {https://arxiv.org/abs/2601.10161}
}

@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}
}
```