README.md

---
base_model:
- google/muril-large-cased
datasets:
- prachuryyaIITG/APTFiNER
language:
- as
license: mit
metrics:
- f1
- precision
- recall
pipeline_tag: token-classification
tags:
- NER
- Named_Entity_Recognition
pretty_name: APTFiNER Assamese XLM-R
library_name: transformers
---

**This model is fine-tuned on the Assamese APTFiNER dataset for Fine-grained Named Entity Recognition.**

It is part of the **AWED-FiNER** collection, as 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).

- **Code:** [GitHub - AWED-FiNER](https://github.com/PrachuryyaKaushik/AWED-FiNER)
- **Interactive Demo:** [Hugging Face Space](https://huggingface.co/spaces/prachuryyaIITG/AWED-FiNER)

The tagset of [MultiCoNER2](https://huggingface.co/datasets/MultiCoNER/multiconer_v2) 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: 62.62 <br>
Recall: 67.98 <br>
**F1: 65.19** <br>

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

## Contributors
[Prachuryya Kaushik](https://www.linkedin.com/in/pkabundant/) <br>
[Adittya Gupta](https://www.linkedin.com/in/adittya-gupta-b64356224/) <br>
[Ajanta Maurya](https://www.linkedin.com/in/ajanta-maurya/) <br>
[Gautam Sharma](https://www.linkedin.com/in/g-s01/) <br>
[Prof. V Vijaya Saradhi](https://www.linkedin.com/in/vijaya-saradhi-a90a604/) <br>
[Prof. Ashish Anand](https://www.linkedin.com/in/anandashish/)

APTFiNER is a part of the [AWED-FiNER collection](https://huggingface.co/collections/prachuryyaIITG/awed-finer). Please check: [**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

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{kaushik2026aptfiner,
  title={APTFiNER: Annotation Preserving Translation for Fine-grained Named Entity Recognition},
  author={Kaushik, Prachuryya and Gupta, Adittya and Maurya, Ajanta and Sharma, Gautam and Saradhi, Vijaya V and Anand, Ashish},
  booktitle={Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  volume={15},
  year={2026}
}

@misc{kaushik2026awedfineragentswebapplications,
      title={AWED-FiNER: Agents, Web applications, and Expert Detectors for 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},
  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}
}
```