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@@ -31,7 +31,7 @@ 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='aioxlabs/toumbert')
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  >>> unmasker("rais wa [MASK] ya tanzania.")
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@@ -41,8 +41,8 @@ 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('aioxlabs/toumbert')
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- model = BertModel.from_pretrained("aioxlabs/toumbert")
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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)
@@ -52,8 +52,8 @@ and in TensorFlow:
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  ```python
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  from transformers import BertTokenizer, TFBertModel
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- tokenizer = BertTokenizer.from_pretrained('aioxlabs/toumbert')
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- model = TFBertModel.from_pretrained("aioxlabs/toumbert")
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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/toumbert')
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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/toumbert')
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+ model = BertModel.from_pretrained("nairaxo/toumbert")
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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/toumbert')
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+ model = TFBertModel.from_pretrained("nairaxo/toumbert")
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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)