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Update README.md

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@@ -116,7 +116,9 @@ When testing, the prompt prefix is added to tell the model what role it is and w
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  <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- Success compilation rate: $\frac{\text{\#Success compilation}}{\text{\#Total compilation}}\times 100\%$. The uncessful compilation is rather LaTeX failure or the timeout case (compilation time > 20s).
 
 
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  ### Results
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  <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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  - **Hardware Type:** Nvidia A100 80G
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  - **Hours used:** 1h = 10min training + 50min testing
 
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  <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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+ Success compilation rate:
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+ $$\frac{\text{\#Success compilation}}{\text{\#Total compilation}}\times 100\%$$
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+ The uncessful compilation is rather LaTeX failure or the timeout case (compilation time > 20s).
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  ### Results
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  <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute).
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  - **Hardware Type:** Nvidia A100 80G
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  - **Hours used:** 1h = 10min training + 50min testing