updated readme.md

README.md

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 license: mit
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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-BigCloneBench in Semeru
+
+
+# CodeXGLUE -- Clone Detection (BCB)
+
+## Task Definition
+
+Given two codes as the input, the task is to do binary classification (0/1), where 1 stands for semantic equivalence and 0 for others. Models are evaluated by F1 score.
+
+## Updates
+
+2021-9-13: We have update the evaluater script. Since it's a binary classification, we use binary F1 score instead of "macro" F1 score.
+
+## Dataset
+
+The dataset we use is [BigCloneBench](https://www.cs.usask.ca/faculty/croy/papers/2014/SvajlenkoICSME2014BigERA.pdf) and filtered following the paper [Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree](https://arxiv.org/pdf/2002.08653.pdf).
+
+### Data Format
+
+1. dataset/data.jsonl is stored in jsonlines format. Each line in the uncompressed file represents one function.  One row is illustrated below.
+
+   - **func:** the function
+
+   - **idx:** index of the example
+
+2. train.txt/valid.txt/test.txt provide examples, stored in the following format:    idx1	idx2	label
+
+### Data Statistics
+
+Data statistics of the dataset are shown in the below table:
+
+|       | #Examples |
+| ----- | :-------: |
+| Train |  901,028  |
+| Dev   |  415,416  |
+| Test  |  415,416  |
+
+
+
+
+
+## Reference
+<pre><code>@inproceedings{svajlenko2014towards,
+  title={Towards a big data curated benchmark of inter-project code clones},
+  author={Svajlenko, Jeffrey and Islam, Judith F and Keivanloo, Iman and Roy, Chanchal K and Mia, Mohammad Mamun},
+  booktitle={2014 IEEE International Conference on Software Maintenance and Evolution},
+  pages={476--480},
+  year={2014},
+  organization={IEEE}
+}
+
+@inproceedings{wang2020detecting,
+  title={Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree},
+  author={Wang, Wenhan and Li, Ge and Ma, Bo and Xia, Xin and Jin, Zhi},
+  booktitle={2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER)},
+  pages={261--271},
+  year={2020},
+  organization={IEEE}
+}</code></pre>