A Roberta-based language model specially designed for Setswana, using the new PuoData dataset.
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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
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')Learn more about the dataset in the Dataset Folder
| Model | 5-fold Cross Validation F1 | Test F1 |
|---|---|---|
| Logistic Regression + TFIDF | 60.1 | 56.2 |
| NCHLT TSN RoBERTa | 64.7 | 60.3 |
| PuoBERTa | 63.8 | 62.9 |
| PuoBERTaJW300 | 66.2 | 65.4 |
Downstream News Categorisation model 🤗 https://huggingface.co/dsfsi/PuoBERTa-News
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 |
Downstream POS model 🤗 https://huggingface.co/dsfsi/PuoBERTa-POS
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 |
Downstream NER model 🤗 https://huggingface.co/dsfsi/PuoBERTa-NER
We used the PuoData dataset, a rich source of Setswana text, ensuring that our model is well-trained and culturally attuned.
Github, 🤗 https://huggingface.co/datasets/dsfsi/PuoData
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}
}
Your contributions are welcome! Feel free to improve the model.
Vukosi Marivate
For more details, reach out or check our website.
Email: [email protected]
Enjoy exploring Setswana through AI!