Edit model card

PuoBERTa-NER: A Setswana Langage Model Finetuned on MasakhaNER-2 for Named Entity Recognition.

Zenodo doi badge arXiv 🤗 https://huggingface.co/dsfsi/PuoBERTa

A Roberta-based language model finetuned on MasakhaneNER-2 for Named Entity Recognition.

Based on https://huggingface.co/dsfsi/PuoBERTa

Model Details

Model Description

This is a POS model trained on Setswana based on PuoBERTa and fineruned on MasakhaNER-2 Setswana.

  • Developed by: Vukosi Marivate (@vukosi), Moseli Mots'Oehli (@MoseliMotsoehli) , Valencia Wagner, Richard Lastrucci and Isheanesu Dzingirai
  • Model type: RoBERTa Model
  • Language(s) (NLP): Setswana
  • License: CC BY 4.0

Model Performance

Performance of models on the MasakhaNER-2 downstream task.

Model Test Performance (f1 score)
Multilingual Models
AfriBERTa 83.2
AfroXLMR-base 87.7
AfroXLMR-large 89.4
Monolingual Models
NCHLT TSN RoBERTa 74.2
PuoBERTa 78.2
PuoBERTa+JW300 80.2

Usage

Use this model for Part of Speech Tagging for Setswana.


Citation Information

Bibtex Refrence

@inproceedings{marivate2023puoberta,
  title   = {PuoBERTa: Training and evaluation of a curated language model for Setswana},
  author  = {Vukosi Marivate and Moseli Mots'Oehli and Valencia Wagner and Richard Lastrucci and Isheanesu Dzingirai},
  year    = {2023},
  booktitle= {Artificial Intelligence Research. SACAIR 2023. Communications in Computer and Information Science},
  url= {https://link.springer.com/chapter/10.1007/978-3-031-49002-6_17},
  keywords = {NLP},
  preprint_url = {https://arxiv.org/abs/2310.09141},
  dataset_url = {https://github.com/dsfsi/PuoBERTa},
  software_url = {https://huggingface.co/dsfsi/PuoBERTa}
}

Contributing

Your contributions are welcome! Feel free to improve the model.

Model Card Authors

Vukosi Marivate

Model Card Contact

For more details, reach out or check our website.

Email: vukosi.marivate@cs.up.ac.za

Enjoy exploring Setswana through AI!

Downloads last month
6
Safetensors
Model size
82.9M params
Tensor type
F32
·
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Datasets used to train dsfsi/PuoBERTa-NER