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batteryonlybert-cased

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@@ -55,7 +55,7 @@ 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('batterydata/batteryonlybert-uncased')
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- model = BertModel.from_pretrained("batterydata/batteryonlybert-uncased")
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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)
@@ -66,7 +66,7 @@ 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('batterydata/batteryonlybert-uncased')
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- model = TFBertModel.from_pretrained(batterydata/batteryonlybert-uncased")
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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 BertTokenizer, BertModel
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  tokenizer = BertTokenizer.from_pretrained('batterydata/batteryonlybert-uncased')
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+ model = BertModel.from_pretrained('batterydata/batteryonlybert-uncased')
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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('batterydata/batteryonlybert-uncased')
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+ model = TFBertModel.from_pretrained('batterydata/batteryonlybert-uncased')
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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)