Updated readme.md

README.md

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+### Dataset is imported from CodeXGLUE and pre-processed using their script.
+
+# Where to find in Semeru:
+The dataset can be found at /nfs/semeru/semeru_datasets/code_xglue/code-to-code/code-refinement/data/medium in Semeru
+
+## Task Definition
+
+Code refinement aims to automatically fix bugs in the code, which can contribute to reducing the cost of bug-fixes for developers.
+In CodeXGLUE, given a piece of Java code with bugs, the task is to remove the bugs to output the refined code. 
+Models are evaluated by BLEU scores, accuracy (exactly match) and [CodeBLEU](https://github.com/microsoft/CodeXGLUE/blob/main/code-to-code-trans/CodeBLEU.MD).
+
+## Dataset
+
+We use the dataset released by this paper(https://arxiv.org/pdf/1812.08693.pdf). The source side is a Java function with bugs and the target side is the refined one. 
+All the function and variable names are normalized. Their dataset contains two subsets ( i.e.small and medium) based on the function length. This dataset is medium.
+
+
+
+### Data Statistics
+
+Data statistics of this dataset are shown in the below table:
+
+|          | #Examples |
+| -------  | :-------: |
+|          |   Medium  |
+|  Train   |   52,364  |
+|  Valid   |    6,545  |
+|   Test   |    6,545  |
+
+# Reference
+<pre><code>@article{tufano2019empirical,
+  title={An empirical study on learning bug-fixing patches in the wild via neural machine translation},
+  author={Tufano, Michele and Watson, Cody and Bavota, Gabriele and Penta, Massimiliano Di and White, Martin and Poshyvanyk, Denys},
+  journal={ACM Transactions on Software Engineering and Methodology (TOSEM)},
+  volume={28},
+  number={4},
+  pages={1--29},
+  year={2019},
+  publisher={ACM New York, NY, USA}
+}</code></pre>
+
+