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--- |
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license: mit |
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language: |
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- xh |
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- zu |
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- nr |
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- ss |
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--- |
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Usage: |
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1. For mask prediction |
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``` |
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tokenizer = AutoTokenizer.from_pretrained("francois-meyer/nguni-xlmr-large") |
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model = XLMRobertaForMaskedLM.from_pretrained("francois-meyer/nguni-xlmr-large") |
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text = "A test <mask> for the nguni model." ## Replace with any sentence from the Nguni Languages with mask tokens. |
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inputs = tokenizer(text, return_tensors="pt") |
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with torch.no_grad(): |
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logits = model(**inputs).logits |
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mask_token_index = (inputs.input_ids == tokenizer.mask_token_id)[0].nonzero(as_tuple=True)[0] |
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predicted_token_id = logits[0, mask_token_index].argmax(axis=-1) |
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print(tokenizer.decode(predicted_token_id)) |
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``` |
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2. For any other task, you might want to fine-tune the model in the same way you fine-tune a BERT/XLMR model. |
