DOI: 10.5281/zenodo.8434795
title: PuoBERTa + PuoBERTaJW300 Setswana Language Models

description: PuoBERTa + PuoBERTaJW300: Setswana Language Models
A Roberta-based language model specially designed for Setswana, using the new PuoData dataset (PuoBERTa) and PuoData + JW300 TSN (PuoBERTaJW300)
Cite 
@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} }
Model Details
Model Description
This is a masked language model trained on Setswana corpora, making it a valuable tool for a range of downstream applications from translation to content creation. It's powered by the PuoData dataset to ensure accuracy and cultural relevance.

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
Usage
Use this model filling in masks or finetune for downstream tasks. Here's a simple example for masked prediction:
from transformers import RobertaTokenizer, RobertaModel # Load model and tokenizer model = RobertaModel.from_pretrained('dsfsi/PuoBERTa') tokenizer = RobertaTokenizer.from_pretrained('dsfsi/PuoBERTa')
 
Downstream Use
Downstream Performance
MasakhaPOS
Performance of models on the MasakhaPOS downstream task.
Model Test Performance 
Multilingual Models
AfroLM
83.8
AfriBERTa
82.5
AfroXLMR-base
82.7
AfroXLMR-large
83.0
Monolingual Models
NCHLT TSN RoBERTa
82.3
PuoBERTa
83.4
PuoBERTa+JW300
84.1
 
MasakhaNER
Performance of models on the MasakhaNER 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
 
Dataset
We used the PuoData dataset, a rich source of Setswana text, ensuring that our model is well-trained and culturally attuned.
Citation Information
Bibtex Reference
@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!