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--- |
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license: cc |
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language: |
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- nso |
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metrics: |
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- perplexity |
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tags: |
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- sepedi |
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- sesotho sa leboa |
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- northen sotho |
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- south africa |
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- bantu |
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- xlm-roberta |
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library_name: transformers |
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widget: |
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- text: "mopresidente wa <mask> wa afrika-borwa" |
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--- |
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# Zabantu - Sepedi |
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This is a variant of [Zabantu](https://huggingface.co/dsfsi/zabantu-bantu-250m) pre-trained on a monolingual dataset of Sepedi(nso) sentences on a transformer network |
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with 120 million traininable parameters. |
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# Usage Example(s) |
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```python |
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from transformers import pipeline |
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# Initialize the pipeline for masked language model |
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unmasker = pipeline('fill-mask', model='dsfsi/zabantu-nso-120m') |
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# The Sepedi sentence with a masked token |
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sample_sentences = ["mopresidente wa <mask> wa afrika-borwa", # original token: maloba |
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"bašomedi ba polase ya dinamune ya zebediela citrus ba hlomile magato a <mask> malebana le go se sepetšwe botse ga dilo ka polaseng eo." # original token: boipelaetšo |
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] |
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# Perform the fill-mask task |
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results = unmasker(sentence) |
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# Display the results |
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for result in results: |
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print(f"Predicted word: {result['token_str']} - Score: {result['score']}") |
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print(f"Full sentence: {result['sequence']}\n") |
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print("=" * 80) |
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``` |
