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/Clone-detection-POJ-104 in Semeru
+
+
+# CodeXGLUE -- Clone Detection (POJ-104)
+
+
+## Task Definition
+
+Given a code and a collection of candidates as the input, the task is to return Top K codes with the same semantic. Models are evaluated by MAP@R score. MAP@R is defined as the mean of average precision scores, each of which is evaluated for retrieving R most similar samples given a query. For a code (query), R is the number of other codes in the same class, i.e. R=499 in this dataset.
+
+
+## Dataset
+
+We use [POJ-104](https://arxiv.org/pdf/1409.5718.pdf) dataset on this task.
+
+
+### Data Format
+
+
+For each file, each line in the uncompressed file represents one function.  One row is illustrated below.
+
+   - **code:** the source code
+   - **label:** the number of problem that the source code solves
+   - **index:** the index of example
+
+### Data Statistics
+
+Data statistics of the dataset are shown in the below table:
+
+|       | #Problems | #Examples |
+| ----- | --------- | :-------: |
+| Train | 64        |  32,000   |
+| Dev   | 16        |   8,000   |
+| Test  | 24        |  12,000   |
+
+## Reference
+<pre><code>@inproceedings{mou2016convolutional,
+  title={Convolutional neural networks over tree structures for programming language processing},
+  author={Mou, Lili and Li, Ge and Zhang, Lu and Wang, Tao and Jin, Zhi},
+  booktitle={Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence},
+  pages={1287--1293},
+  year={2016}
+}</code></pre>