Datasets:

google
/

code_x_glue_tc_nl_code_search_adv

	---
	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: 820714108
	num_examples: 251820
	- name: validation
	num_bytes: 23468758
	num_examples: 9604
	- name: test
	num_bytes: 47433608
	num_examples: 19210
	download_size: 316235421
	dataset_size: 891616474
	configs:
	- config_name: default
	data_files:
	- split: train
	path: data/train-*
	- split: validation
	path: data/validation-*
	- split: test
	path: data/test-*
	---
	# Dataset Card for "code_x_glue_tc_nl_code_search_adv"

	## Table of Contents
	- [Dataset Description](#dataset-description)
	- [Dataset Summary](#dataset-summary)
	- [Supported Tasks and Leaderboards](#supported-tasks)
	- [Languages](#languages)
	- [Dataset Structure](#dataset-structure)
	- [Data Instances](#data-instances)
	- [Data Fields](#data-fields)
	- [Data Splits](#data-splits-sample-size)
	- [Dataset Creation](#dataset-creation)
	- [Curation Rationale](#curation-rationale)
	- [Source Data](#source-data)
	- [Annotations](#annotations)
	- [Personal and Sensitive Information](#personal-and-sensitive-information)
	- [Considerations for Using the Data](#considerations-for-using-the-data)
	- [Social Impact of Dataset](#social-impact-of-dataset)
	- [Discussion of Biases](#discussion-of-biases)
	- [Other Known Limitations](#other-known-limitations)
	- [Additional Information](#additional-information)
	- [Dataset Curators](#dataset-curators)
	- [Licensing Information](#licensing-information)
	- [Citation Information](#citation-information)
	- [Contributions](#contributions)

	## 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 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.