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google/code_x_glue_tc_nl_code_search_adv

Dataset Card for "code_x_glue_tc_nl_code_search_adv" Dataset Summary CodeXGLUE NL-code-search-Adv dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Text-Code/NL-code-search-Adv The dataset we use comes from CodeSearchNet and we filter the dataset as the following: Remove examples that codes cannot be parsed into an abstract syntax tree. Remove examples that #tokens of documents is < 3 or >256 Remove examples that documents contain special… See the full description on the dataset page: https://huggingface.co/datasets/google/code_x_glue_tc_nl_code_search_adv.

sourceHugging Facec-udaupdated 3y agoView on Hugging Face
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Dataset Card

Dataset Card for "codexgluetcnlcodesearch_adv"

Table of Contents

Dataset Description

  • —Homepage: https://github.com/microsoft/CodeXGLUE/tree/main/Text-Code/NL-code-search-Adv
  • —Paper: https://arxiv.org/abs/2102.04664

Dataset Summary

CodeXGLUE NL-code-search-Adv dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Text-Code/NL-code-search-Adv

The dataset we use comes from CodeSearchNet and we filter the dataset as the following:

  • —Remove examples that codes cannot be parsed into an abstract syntax tree.
  • —Remove examples that #tokens of documents is < 3 or >256
  • —Remove examples that documents contain special tokens (e.g. <img ...> or https:...)
  • —Remove examples that documents are not English.

Supported Tasks and Leaderboards

  • —document-retrieval: The dataset can be used to train a model for retrieving top-k codes from a given English natural language query.

Languages

  • —Python programming language
  • —English natural language

Dataset Structure

Data Instances

An example of 'validation' looks as follows.

{
    "argument_list": "", 
    "code": "def Func(arg_0, arg_1='.', arg_2=True, arg_3=False, **arg_4):\n    \"\"\"Downloads Dailymotion videos by URL.\n    \"\"\"\n\n    arg_5 = get_content(rebuilt_url(arg_0))\n    arg_6 = json.loads(match1(arg_5, r'qualities\":({.+?}),\"'))\n    arg_7 = match1(arg_5, r'\"video_title\"\\s*:\\s*\"([^\"]+)\"') or \\\n            match1(arg_5, r'\"title\"\\s*:\\s*\"([^\"]+)\"')\n    arg_7 = unicodize(arg_7)\n\n    for arg_8 in ['1080','720','480','380','240','144','auto']:\n        try:\n            arg_9 = arg_6[arg_8][1][\"url\"]\n            if arg_9:\n                break\n        except KeyError:\n            pass\n\n    arg_10, arg_11, arg_12 = url_info(arg_9)\n\n    print_info(site_info, arg_7, arg_10, arg_12)\n    if not arg_3:\n        download_urls([arg_9], arg_7, arg_11, arg_12, arg_1=arg_1, arg_2=arg_2)", 
    "code_tokens": ["def", "Func", "(", "arg_0", ",", "arg_1", "=", "'.'", ",", "arg_2", "=", "True", ",", "arg_3", "=", "False", ",", "**", "arg_4", ")", ":", "arg_5", "=", "get_content", "(", "rebuilt_url", "(", "arg_0", ")", ")", "arg_6", "=", "json", ".", "loads", "(", "match1", "(", "arg_5", ",", "r'qualities\":({.+?}),\"'", ")", ")", "arg_7", "=", "match1", "(", "arg_5", ",", "r'\"video_title\"\\s*:\\s*\"([^\"]+)\"'", ")", "or", "match1", "(", "arg_5", ",", "r'\"title\"\\s*:\\s*\"([^\"]+)\"'", ")", "arg_7", "=", "unicodize", "(", "arg_7", ")", "for", "arg_8", "in", "[", "'1080'", ",", "'720'", ",", "'480'", ",", "'380'", ",", "'240'", ",", "'144'", ",", "'auto'", "]", ":", "try", ":", "arg_9", "=", "arg_6", "[", "arg_8", "]", "[", "1", "]", "[", "\"url\"", "]", "if", "arg_9", ":", "break", "except", "KeyError", ":", "pass", "arg_10", ",", "arg_11", ",", "arg_12", "=", "url_info", "(", "arg_9", ")", "print_info", "(", "site_info", ",", "arg_7", ",", "arg_10", ",", "arg_12", ")", "if", "not", "arg_3", ":", "download_urls", "(", "[", "arg_9", "]", ",", "arg_7", ",", "arg_11", ",", "arg_12", ",", "arg_1", "=", "arg_1", ",", "arg_2", "=", "arg_2", ")"], 
    "docstring": "Downloads Dailymotion videos by URL.", 
    "docstring_summary": "Downloads Dailymotion videos by URL.", 
    "docstring_tokens": ["Downloads", "Dailymotion", "videos", "by", "URL", "."], 
    "func_name": "", 
    "id": 0, 
    "identifier": "dailymotion_download", 
    "language": "python", 
    "nwo": "soimort/you-get", 
    "original_string": "", 
    "parameters": "(url, output_dir='.', merge=True, info_only=False, **kwargs)", 
    "path": "src/you_get/extractors/dailymotion.py", 
    "repo": "", 
    "return_statement": "", 
    "score": 0.9997601509094238, 
    "sha": "b746ac01c9f39de94cac2d56f665285b0523b974", 
    "url": "https://github.com/soimort/you-get/blob/b746ac01c9f39de94cac2d56f665285b0523b974/src/you_get/extractors/dailymotion.py#L13-L35"
}

Data Fields

In the following each data field in go is explained for each config. The data fields are the same among all splits.

default
field nametypedescription
idint32Index of the sample
repostringrepo: the owner/repo
pathstringpath: the full path to the original file
func_namestringfunc_name: the function or method name
original_stringstringoriginal_string: the raw string before tokenization or parsing
languagestringlanguage: the programming language
codestringcode/function: the part of the original_string that is code
code_tokensSequence[string]codetokens/functiontokens: tokenized version of code
docstringstringdocstring: the top-level comment or docstring, if it exists in the original string
docstring_tokensSequence[string]docstring_tokens: tokenized version of docstring
shastringsha of the file
urlstringurl of the file
docstring_summarystringSummary of the docstring
parametersstringparameters of the function
return_statementstringreturn statement
argument_liststringlist of arguments of the function
identifierstringidentifier
nwostringnwo
scoredatasets.Value("float"]score for this search

Data Splits

nametrainvalidationtest
default251820960419210

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

Data from CodeSearchNet Challenge dataset. [More Information Needed]

Who are the source language producers?

Software Engineering developers.

Annotations

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

https://github.com/microsoft, https://github.com/madlag

Licensing Information

Computational Use of Data Agreement (C-UDA) License.

Citation Information

@article{DBLP:journals/corr/abs-2102-04664,
  author    = {Shuai Lu and
               Daya Guo and
               Shuo Ren and
               Junjie Huang and
               Alexey Svyatkovskiy and
               Ambrosio Blanco and
               Colin B. Clement and
               Dawn Drain and
               Daxin Jiang and
               Duyu Tang and
               Ge Li and
               Lidong Zhou and
               Linjun Shou and
               Long Zhou and
               Michele Tufano and
               Ming Gong and
               Ming Zhou and
               Nan Duan and
               Neel Sundaresan and
               Shao Kun Deng and
               Shengyu Fu and
               Shujie Liu},
  title     = {CodeXGLUE: {A} Machine Learning Benchmark Dataset for Code Understanding
               and Generation},
  journal   = {CoRR},
  volume    = {abs/2102.04664},
  year      = {2021}
}
@article{husain2019codesearchnet,
  title={Codesearchnet challenge: Evaluating the state of semantic code search},
  author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},
  journal={arXiv preprint arXiv:1909.09436},
  year={2019}
}

Contributions

Thanks to @madlag (and partly also @ncoop57) for adding this dataset.