Datasets:
Tasks:
Text Retrieval
Formats:
parquet
Sub-tasks:
document-retrieval
Size:
100K - 1M
ArXiv:
License:
metadata
annotations_creators:
- found
language_creators:
- found
language:
- code
- en
license:
- c-uda
multilinguality:
- other-programming-languages
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-retrieval
task_ids:
- document-retrieval
pretty_name: CodeXGlueTcNlCodeSearchAdv
dataset_info:
features:
- name: id
dtype: int32
- name: repo
dtype: string
- name: path
dtype: string
- name: func_name
dtype: string
- name: original_string
dtype: string
- name: language
dtype: string
- name: code
dtype: string
- name: code_tokens
sequence: string
- name: docstring
dtype: string
- name: docstring_tokens
sequence: string
- name: sha
dtype: string
- name: url
dtype: string
- name: docstring_summary
dtype: string
- name: parameters
dtype: string
- name: return_statement
dtype: string
- name: argument_list
dtype: string
- name: identifier
dtype: string
- name: nwo
dtype: string
- name: score
dtype: float32
splits:
- name: train
num_bytes: 820716084
num_examples: 251820
- name: validation
num_bytes: 23468834
num_examples: 9604
- name: test
num_bytes: 47433760
num_examples: 19210
download_size: 966025624
dataset_size: 891618678
Dataset Card for "code_x_glue_tc_nl_code_search_adv"
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
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 name | type | description |
|---|---|---|
| id | int32 | Index of the sample |
| repo | string | repo: the owner/repo |
| path | string | path: the full path to the original file |
| func_name | string | func_name: the function or method name |
| original_string | string | original_string: the raw string before tokenization or parsing |
| language | string | language: the programming language |
| code | string | code/function: the part of the original_string that is code |
| code_tokens | Sequence[string] | code_tokens/function_tokens: tokenized version of code |
| docstring | string | docstring: the top-level comment or docstring, if it exists in the original string |
| docstring_tokens | Sequence[string] | docstring_tokens: tokenized version of docstring |
| sha | string | sha of the file |
| url | string | url of the file |
| docstring_summary | string | Summary of the docstring |
| parameters | string | parameters of the function |
| return_statement | string | return statement |
| argument_list | string | list of arguments of the function |
| identifier | string | identifier |
| nwo | string | nwo |
| score | datasets.Value("float"] | score for this search |
Data Splits
| name | train | validation | test |
|---|---|---|---|
| default | 251820 | 9604 | 19210 |
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.