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MathBERT

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@@ -46,8 +46,8 @@ Note that this model is primarily aimed at being fine-tuned on math-related task
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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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  ```from transformers import BertTokenizer, BertModel
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- tokenizer = BertTokenizer.from_pretrained('tbs17/MathBERT-custom')
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- model = BertModel.from_pretrained("tbs17/MathBERT-custom")
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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)
@@ -56,8 +56,8 @@ and in TensorFlow:
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  ```
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  from transformers import BertTokenizer, TFBertModel
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- tokenizer = BertTokenizer.from_pretrained('tbs17/MathBERT-custom')
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- model = TFBertModel.from_pretrained("tbs17/MathBERT-custom")
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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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  ```from transformers import BertTokenizer, BertModel
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+ tokenizer = BertTokenizer.from_pretrained('tbs17/MathBERT')
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+ model = BertModel.from_pretrained("tbs17/MathBERT")
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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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  ```
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  from transformers import BertTokenizer, TFBertModel
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+ tokenizer = BertTokenizer.from_pretrained('tbs17/MathBERT')
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+ model = TFBertModel.from_pretrained("tbs17/MathBERT")
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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)