PuoBertaJW300: A curated Setswana Language Model (trained on PuoData + JW300 Setswana)

A Roberta-based language model specially designed for Setswana, using the new PuoData dataset + JW300 corpora.

NOTE: If you are looking for the model without JW300, go to 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/PuoBERTaJW300')
tokenizer = RobertaTokenizer.from_pretrained('dsfsi/PuoBERTaJW300')

Downstream Use

Downstream Performance

Daily News Dikgang

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

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

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

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://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.

Email: [email protected]

Enjoy exploring Setswana through AI!

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PuoBERTaJW300