PuoBERTa + PuoBERTaJW300 Setswana Language Models

PuoBERTa + PuoBERTaJW300: Setswana Language Models A Roberta-based language model specially designed for Setswana, using the new PuoData dataset (PuoBERTa) and PuoData + JW300 TSN (PuoBERTaJW300) Cite @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} } 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/PuoBERTa') tokenizer = RobertaTokenizer.from_pretrained('dsfsi/PuoBERTa') Downstream Use Downstream Performance 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 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 Dataset We used the PuoData dataset, a rich source of Setswana text, ensuring that our model is well-trained and culturally attuned. 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: [e-mail] Enjoy exploring Setswana through AI!