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Update README.md

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  ## About
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- Here we share a pretrained bert model that is aware of math tokens. The math tokens are treated specially and are tokenized using [pya0](https://github.com/approach0/pya0), which adds very limited new tokens for latex markup (total vocabulary is just 31061).
 
 
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  ### Usage
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  Download and try it out
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  ### Test file format
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  Modify the test examples in `test.txt` to play with it.
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- The test file is tab separated, the first column is additional positions you want to mask for the right-side sentence (useful for masking tokens in math markups). An zero means no additional mask positions.
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  ### Example output
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  ![](https://i.imgur.com/xpl87KO.png)
 
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  ## About
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+ Here we share a pretrained BERT model that is aware of math tokens. The math tokens are treated specially and tokenized using [pya0](https://github.com/approach0/pya0), which adds very limited new tokens for latex markup (total vocabulary is just 31,061).
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+ This model is trained on 4 x 2 Tesla V100 with a total batch size of 64, using Math StackExchange data with 2.7 million sentence pairs for 7 epochs.
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  ### Usage
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  Download and try it out
 
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  ### Test file format
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  Modify the test examples in `test.txt` to play with it.
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+ The test file is tab-separated, the first column is additional positions you want to mask for the right-side sentence (useful for masking tokens in math markups). A zero means no additional mask positions.
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  ### Example output
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  ![](https://i.imgur.com/xpl87KO.png)