metadata

language:
  - code
  - en
multilinguality:
  - multiprogramming languages
task_categories:
  - text-generation
license: mit
dataset_info:
  features:
    - name: identifier
      dtype: string
    - name: return_type
      dtype: string
    - name: repo
      dtype: string
    - name: path
      dtype: string
    - name: language
      dtype: string
    - name: code
      dtype: string
    - name: code_tokens
      dtype: string
    - name: original_docstring
      dtype: string
    - name: comment
      dtype: string
    - name: docstring_tokens
      dtype: string
    - name: docstring
      dtype: string
    - name: original_string
      dtype: string
pretty_name: The Vault Function
viewer: false

Dataset Description

Repository: FSoft-AI4Code/TheVault
Paper: The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation
Contact: support.ailab@fpt.com
Website: https://www.fpt-aicenter.com/ai-residency/

The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation

Dataset Summary

The Vault is a multilingual code-text dataset with over 34 million pairs ìn function-level covering 10 popular programming languages. It is the largest corpus containing parallel code-text data. By building upon The Stack, a massive raw code sample collection, the Vault offers a comprehensive and clean resource for advancing research in code understanding and generation. It provides a high-quality dataset that includes code-text pairs at multiple levels, such as class and inline-level, in addition to the function level. The Vault can serve many purposes at multiple levels.

Supported Tasks

The Vault can be used for pretraining LLMs or downstream code-text interaction tasks. A number of tasks related to code understanding and geneartion can be constructed using The Vault such as code summarization, text-to-code generation and code search.

Languages

The natural language text (docstring) is in English.

10 programming languages are supported in The Vault: Python, Java, JavaScript, PHP, C, C#, C++, Go, Ruby, Rust

Dataset Structure

Data Instances

{

    "hexsha": "5c47f0b4c173a8fd03e4e633d9b3dd8211e67ad0",
    "repo": "neumanna94/beepboop",
    "path": "js/scripts.js",
    "license": [
        "MIT"
    ],
    "language": "JavaScript",
    "identifier": "beepBoopSelector",
    "code": "function beepBoopSelector(inputString, bbFunction){\n  if(bbFunction==1){\n    return beepBoop(inputString);\n  } else if(bbFunction==2){\n    return beepBoop2(inputString);\n  } else if(bbFunction==3){\n    return beepBoop3(inputString);\n  } else {\n  }\n}",
    "code_tokens": [
        "function",
        "beepBoopSelector",
        "(",
        "inputString",
        ",",
        "bbFunction",
        ")",
        "{",
        "if",
        "(",
        "bbFunction",
        "==",
        "1",
        ")",
        "{",
        "return",
        "beepBoop",
        "(",
        "inputString",
        ")",
        ";",
        "}",
        "else",
        "if",
        "(",
        "bbFunction",
        "==",
        "2",
        ")",
        "{",
        "return",
        "beepBoop2",
        "(",
        "inputString",
        ")",
        ";",
        "}",
        "else",
        "if",
        "(",
        "bbFunction",
        "==",
        "3",
        ")",
        "{",
        "return",
        "beepBoop3",
        "(",
        "inputString",
        ")",
        ";",
        "}",
        "else",
        "{",
        "}",
        "}"
    ],
}

Data Fields

Data fields for function level:

hexsha (string): the unique git hash of file
repo (string): the owner/repo
path (string): the full path to the original file
license (list): licenses in the repo
language (string): the programming language
identifier (string): the function or method name
code (string): the part of the original that is code
code_tokens (list): tokenized version of code
original_comment (string): original text of comment ,
comment (string): clean version of comment,
comment_tokens (list): tokenized version of comment,
start_point (int): start position of original_comment in code,
end_point (int): end position of original_comment in code,
prev_context (dict): block of code before original_comment,
next_context (dict): block of code after original_comment

Data Splits

In this repo, the inline level data is not split, and contain in only train set.

Dataset Statistics

	train/small	train/medium	train/full	validation	test	total
Python	370,657	1,952,110	7,772,647	30,992	21,652	7,825,291
Java	351,213	1,612,366	6,629,193	22,677	15,552	6,667,422
JavaScript	82,931	404,729	1,640,416	22,044	21,108	1,683,568
PHP	236,638	1,155,476	4,656,371	21,375	19,010	4,696,756
C	105,978	381,207	1,639,319	27,525	19,122	1,685,966
C#	141,090	783,166	3,305,891	24,787	19,638	3,350,316
C++	87,420	410,907	1,671,268	20,011	18,169	1,709,448
Go	267,535	1,319,547	5,109,020	19,102	25,314	5,153,436
Ruby	23,921	112,574	424,339	17,338	19,908	461,585
Rust	35,367	224,015	825,130	16,716	23,141	864,987
TOTAL	1,702,750	8,356,097	33,673,594	222,567	202,614	34,098,775

Usage

You can load The Vault dataset using datasets library: pip install datasets

from datasets import load_dataset

# Load full function level dataset (40M samples)
dataset = load_dataset("Fsoft-AIC/the-vault-inline")


# specific language (e.g. Python) 
dataset = load_dataset("Fsoft-AIC/the-vault-inline", languages=['Python'])

# dataset streaming
data = load_dataset("Fsoft-AIC/the-vault-inline", streaming= True)
for sample in iter(data['train']): 
    print(sample)

A back up dataset can be downloaded in azure storage. See Download The Vault from Azure blob storage.

Additional information

Licensing Information

MIT License

Citation Information

@article{manh2023vault,
  title={The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation},
  author={Manh, Dung Nguyen and Hai, Nam Le and Dau, Anh TV and Nguyen, Anh Minh and Nghiem, Khanh and Guo, Jin and Bui, Nghi DQ},
  journal={arXiv preprint arXiv:2305.06156},
  year={2023}
}

Contributions

This dataset is developed by FSOFT AI4Code team.

Datasets:

Fsoft-AIC
/

the-vault-inline

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