https://github.com/dsfsi/puoberta
A Roberta-based language model specially designed for Setswana, using the new PuoData dataset.
https://github.com/dsfsi/puoberta
african-languages africannlp dsfsi-datasets nlproc setswana tn tsn
Last synced: 3 months ago
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A Roberta-based language model specially designed for Setswana, using the new PuoData dataset.
- Host: GitHub
- URL: https://github.com/dsfsi/puoberta
- Owner: dsfsi
- License: other
- Created: 2023-10-12T10:01:10.000Z (about 2 years ago)
- Default Branch: master
- Last Pushed: 2023-12-04T19:04:31.000Z (almost 2 years ago)
- Last Synced: 2025-03-14T18:20:53.943Z (7 months ago)
- Topics: african-languages, africannlp, dsfsi-datasets, nlproc, setswana, tn, tsn
- Language: Makefile
- Homepage: https://huggingface.co/dsfsi/PuoBERTa
- Size: 3.56 MB
- Stars: 4
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# PuoBerta: A curated Setswana Language Model
[](https://doi.org/10.5281/zenodo.8434795) [](https://arxiv.org/abs/2310.09141) 🤗 [https://huggingface.co/dsfsi/PuoBERTa](https://huggingface.co/dsfsi/PuoBERTa)
A Roberta-based language model specially designed for Setswana, using the new PuoData dataset.
Give Feedback 📑: [DSFSI Resource Feedback Form](https://docs.google.com/forms/d/e/1FAIpQLSf7S36dyAUPx2egmXbFpnTBuzoRulhL5Elu-N1eoMhaO7v10w/formResponse){:target="_blank"}
## 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](https://huggingface.co/@vukosi)), Moseli Mots'Oehli ([@MoseliMotsoehli](https://huggingface.co/@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
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
### Daily News Dikgang
Learn more about the dataset in the [Dataset Folder](daily-news-dikgang)
| **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](https://huggingface.co/dsfsi/PuoBERTa-News)
### 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 |
Downstream POS model 🤗 [https://huggingface.co/dsfsi/PuoBERTa-POS](https://huggingface.co/dsfsi/PuoBERTa-POS)
### 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 |
Downstream NER model 🤗 [https://huggingface.co/dsfsi/PuoBERTa-NER](https://huggingface.co/dsfsi/PuoBERTa-NER)
## Pre-Training Dataset
We used the PuoData dataset, a rich source of Setswana text, ensuring that our model is well-trained and culturally attuned.
[Github](https://github.com/dsfsi/PuoData), 🤗 [https://huggingface.co/datasets/dsfsi/PuoData](https://huggingface.co/datasets/dsfsi/PuoData)
## 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](https://dsfsi.github.io/).
Email: vukosi.marivate@cs.up.ac.za
**Enjoy exploring Setswana through AI!**