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--- |
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license: cc-by-4.0 |
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datasets: |
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- dsfsi/vukuzenzele-monolingual |
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- nchlt |
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- dsfsi/PuoData |
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- dsfsi/gov-za-monolingual |
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language: |
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- tn |
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library_name: transformers |
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pipeline_tag: fill-mask |
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tags: |
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- masked langauge model |
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- setswana |
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--- |
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# PuoBertaJW300: A curated Setswana Language Model (trained on PuoData + JW300 Setswana) |
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[](https://doi.org/10.5281/zenodo.8434795) [](https://arxiv.org/abs/2310.09141) 🤗 [https://huggingface.co/dsfsi/PuoBERTa](https://huggingface.co/dsfsi/PuoBERTa) |
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A Roberta-based language model specially designed for Setswana, using the new PuoData dataset + JW300 corpora. |
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**NOTE**: If you are looking for the model without JW300, go to [https://huggingface.co/dsfsi/PuoBERTa](https://huggingface.co/dsfsi/PuoBERTa) |
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## Model Details |
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### Model Description |
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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. |
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- **Developed by:** Vukosi Marivate ([@vukosi](https://huggingface.co/@vukosi)), Moseli Mots'Oehli ([@MoseliMotsoehli](https://huggingface.co/@MoseliMotsoehli)) , Valencia Wagner, Richard Lastrucci and Isheanesu Dzingirai |
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- **Model type:** RoBERTa Model |
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- **Language(s) (NLP):** Setswana |
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- **License:** CC BY 4.0 |
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### Usage |
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Use this model filling in masks or finetune for downstream tasks. Here’s a simple example for masked prediction: |
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```python |
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from transformers import RobertaTokenizer, RobertaModel |
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# Load model and tokenizer |
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model = RobertaModel.from_pretrained('dsfsi/PuoBERTaJW300') |
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tokenizer = RobertaTokenizer.from_pretrained('dsfsi/PuoBERTaJW300') |
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``` |
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### Downstream Use |
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## Downstream Performance |
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### Daily News Dikgang |
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Learn more about the dataset in the [Dataset Folder](daily-news-dikgang) |
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| **Model** | **5-fold Cross Validation F1** | **Test F1** | |
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|-----------------------------|--------------------------------------|-------------------| |
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| Logistic Regression + TFIDF | 60.1 | 56.2 | |
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| NCHLT TSN RoBERTa | 64.7 | 60.3 | |
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| PuoBERTa | **63.8** | **62.9** | |
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| PuoBERTaJW300 | 66.2 | 65.4 | |
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Downstream News Categorisation model 🤗 [https://huggingface.co/dsfsi/PuoBERTa-News](https://huggingface.co/dsfsi/PuoBERTa-News) |
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### MasakhaPOS |
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Performance of models on the MasakhaPOS downstream task. |
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| Model | Test Performance | |
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|---|---| |
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| **Multilingual Models** | | |
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| AfroLM | 83.8 | |
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| AfriBERTa | 82.5 | |
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| AfroXLMR-base | 82.7 | |
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| AfroXLMR-large | 83.0 | |
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| **Monolingual Models** | | |
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| NCHLT TSN RoBERTa | 82.3 | |
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| PuoBERTa | **83.4** | |
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| PuoBERTa+JW300 | 84.1 | |
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Downstream POS model 🤗 [https://huggingface.co/dsfsi/PuoBERTa-POS](https://huggingface.co/dsfsi/PuoBERTa-POS) |
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### MasakhaNER |
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Performance of models on the MasakhaNER downstream task. |
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| Model | Test Performance (f1 score) | |
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|---|---| |
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| **Multilingual Models** | | |
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| AfriBERTa | 83.2 | |
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| AfroXLMR-base | 87.7 | |
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| AfroXLMR-large | 89.4 | |
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| **Monolingual Models** | | |
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| NCHLT TSN RoBERTa | 74.2 | |
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| PuoBERTa | **78.2** | |
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| PuoBERTa+JW300 | 80.2 | |
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Downstream NER model 🤗 [https://huggingface.co/dsfsi/PuoBERTa-NER](https://huggingface.co/dsfsi/PuoBERTa-NER) |
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## Pre-Training Dataset |
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We used the PuoData dataset, a rich source of Setswana text, ensuring that our model is well-trained and culturally attuned. |
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[Github](https://github.com/dsfsi/PuoData), 🤗 [https://huggingface.co/datasets/dsfsi/PuoData](https://huggingface.co/datasets/dsfsi/PuoData) |
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## Citation Information |
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Bibtex Reference |
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``` |
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@inproceedings{marivate2023puoberta, |
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title = {PuoBERTa: Training and evaluation of a curated language model for Setswana}, |
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author = {Vukosi Marivate and Moseli Mots'Oehli and Valencia Wagner and Richard Lastrucci and Isheanesu Dzingirai}, |
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year = {2023}, |
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booktitle= {Artificial Intelligence Research. SACAIR 2023. Communications in Computer and Information Science}, |
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url= {https://link.springer.com/chapter/10.1007/978-3-031-49002-6_17}, |
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keywords = {NLP}, |
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preprint_url = {https://arxiv.org/abs/2310.09141}, |
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dataset_url = {https://github.com/dsfsi/PuoBERTa}, |
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software_url = {https://huggingface.co/dsfsi/PuoBERTa} |
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} |
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``` |
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## Contributing |
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Your contributions are welcome! Feel free to improve the model. |
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## Model Card Authors |
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Vukosi Marivate |
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## Model Card Contact |
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For more details, reach out or check our [website](https://dsfsi.github.io/). |
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Email: [email protected] |
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**Enjoy exploring Setswana through AI!** |
