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/PuoBERTaJW300')
tokenizer = RobertaTokenizer.from_pretrained('dsfsi/PuoBERTaJW300')
@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
.
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