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:
python
1from transformers import RobertaTokenizer, RobertaModel
23# Load model and tokenizer4model = RobertaModel.from_pretrained('dsfsi/PuoBERTaJW300')5tokenizer = RobertaTokenizer.from_pretrained('dsfsi/PuoBERTaJW300')6
@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.
