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@@ -82,9 +82,9 @@ You can use this model directly with a pipeline for masked language modeling:
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  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, TFBertModel
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  tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
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- model = TFBertModel.from_pretrained("bert-base-cased")
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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)
@@ -93,9 +93,9 @@ output = model(**encoded_input)
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  and in TensorFlow:
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  ```python
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- from transformers import BertTokenizer, BertModel
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  tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
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- model = BertModel.from_pretrained("bert-base-cased")
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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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  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('bert-base-cased')
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+ model = BertModel.from_pretrained("bert-base-cased")
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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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  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('bert-base-cased')
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+ model = TFBertModel.from_pretrained("bert-base-cased")
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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)