---
license: mit
datasets:
- MultiCoNER/multiconer_v2
language:
- en
metrics:
- f1
- precision
- recall
base_model:
- FacebookAI/xlm-roberta-large
pipeline_tag: token-classification
tags:
- NER
- Named_Entity_Recognition
pretty_name: MultiCoNER2 English XLM-RoBERTa
---

**XLM-RoBERTa is fine-tuned on English [MultiCoNER2](https://huggingface.co/datasets/MultiCoNER/multiconer_v2) dataset for Fine-grained Named Entity Recognition.**

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: 78.29 <br>
Recall: 80.94 <br>
**F1: 79.59** <br>

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


## Citation

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

```bibtex
@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}
}

@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}
}