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README.md
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@@ -29,7 +29,7 @@ You can use this model directly with a pipeline for masked language modeling:
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```python
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>>> from transformers import pipeline
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>>> unmasker = pipeline('fill-mask', model='
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>>> unmasker("rais wa [MASK] ya tanzania.")
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@@ -39,8 +39,8 @@ Here is how to use this model to get the features of a given text in PyTorch:
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```python
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from transformers import BertTokenizer, BertModel
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tokenizer = BertTokenizer.from_pretrained('
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model = BertModel.from_pretrained("
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text = "Replace me by any text you'd like."
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encoded_input = tokenizer(text, return_tensors='pt')
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output = model(**encoded_input)
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```python
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from transformers import BertTokenizer, TFBertModel
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tokenizer = BertTokenizer.from_pretrained('
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model = TFBertModel.from_pretrained("
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text = "Replace me by any text you'd like."
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encoded_input = tokenizer(text, return_tensors='tf')
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output = model(encoded_input)
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```python
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>>> from transformers import pipeline
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>>> unmasker = pipeline('fill-mask', model='nairaxo/bantu-bert')
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>>> unmasker("rais wa [MASK] ya tanzania.")
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```python
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from transformers import BertTokenizer, BertModel
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tokenizer = BertTokenizer.from_pretrained('nairaxo/bantu-bert')
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model = BertModel.from_pretrained("nairaxo/bantu-bert")
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text = "Replace me by any text you'd like."
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encoded_input = tokenizer(text, return_tensors='pt')
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output = model(**encoded_input)
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```python
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from transformers import BertTokenizer, TFBertModel
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tokenizer = BertTokenizer.from_pretrained('nairaxo/bantu-bert')
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model = TFBertModel.from_pretrained("nairaxo/bantu-bert")
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text = "Replace me by any text you'd like."
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encoded_input = tokenizer(text, return_tensors='tf')
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output = model(encoded_input)
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