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license: mit
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---
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---
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license: mit
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---
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### Dataset is imported from CodeXGLUE and pre-processed using their script.
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# Where to find in Semeru:
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The dataset can be found at /nfs/semeru/semeru_datasets/code_xglue/code-to-text/ruby in Semeru
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# CodeXGLUE -- Code-To-Text
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## Task Definition
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The task is to generate natural language comments for a code, and evaluted by [smoothed bleu-4](https://www.aclweb.org/anthology/C04-1072.pdf) score.
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## Dataset
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The dataset we use comes from [CodeSearchNet](https://arxiv.org/pdf/1909.09436.pdf) and we filter the dataset as the following:
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- Remove examples that codes cannot be parsed into an abstract syntax tree.
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- Remove examples that #tokens of documents is < 3 or >256
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- Remove examples that documents contain special tokens (e.g. <img ...> or https:...)
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- Remove examples that documents are not English.
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### Data Format
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After preprocessing dataset, you can obtain three .jsonl files, i.e. train.jsonl, valid.jsonl, test.jsonl
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For each file, each line in the uncompressed file represents one function. One row is illustrated below.
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- **repo:** the owner/repo
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- **path:** the full path to the original file
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- **func_name:** the function or method name
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- **original_string:** the raw string before tokenization or parsing
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- **language:** the programming language
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- **code/function:** the part of the `original_string` that is code
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- **code_tokens/function_tokens:** tokenized version of `code`
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- **docstring:** the top-level comment or docstring, if it exists in the original string
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- **docstring_tokens:** tokenized version of `docstring`
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### Data Statistic
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| Programming Language | Training | Dev | Test |
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| :------------------- | :------: | :----: | :----: |
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| Python | 251,820 | 13,914 | 14,918 |
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| PHP | 241,241 | 12,982 | 14,014 |
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| Go | 167,288 | 7,325 | 8,122 |
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| Java | 164,923 | 5,183 | 10,955 |
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| JavaScript | 58,025 | 3,885 | 3,291 |
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| Ruby | 24,927 | 1,400 | 1,261 |
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## Reference
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<pre><code>@article{husain2019codesearchnet,
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title={Codesearchnet challenge: Evaluating the state of semantic code search},
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author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},
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journal={arXiv preprint arXiv:1909.09436},
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year={2019}
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}</code></pre>
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