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README.md
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@@ -44,11 +44,44 @@ tokenizer = RobertaTokenizer.from_pretrained('dsfsi/PuoBERTa')
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### Downstream Use
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## 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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##
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Bibtex Refrence
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### Downstream Use
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## Downstream Performance
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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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### 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 | \textbf{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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## 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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## Citation Information
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Bibtex Refrence
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