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: [email protected]
Website: https://www.fpt-aicenter.com/ai-residency/

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

Dataset Summary

The Vault dataset is a comprehensive, large-scale, multilingual parallel dataset that features high-quality code-text pairs derived from The Stack, the largest permissively-licensed source code dataset.

We provide The Vault which contains code snippets from 10 popular programming languages such as Java, JavaScript, Python, Ruby, Rust, Golang, C#, C++, C, and PHP. This dataset provides multiple code-snippet levels, metadata, and 11 docstring styles for enhanced usability and versatility.

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": "ee1cf38808d3db0ea364b049509a01a65e6e5589",
    "repo": "Waguy02/Boomer-Scripted",
    "path": "python/subprojects/testbed/mlrl/testbed/persistence.py",
    "license": [
        "MIT"
    ],
    "language": "Python",
    "identifier": "__init__",
    "code": "def __init__(self, model_dir: str):\n        \"\"\"\n        :param model_dir: The path of the directory where models should be saved\n        \"\"\"\n        self.model_dir = model_dir",
    "code_tokens": [
        "def",
        "__init__",
        "(",
        "self",
        ",",
        "model_dir",
        ":",
        "str",
        ")",
        ":",
        "\"\"\"\n        :param model_dir: The path of the directory where models should be saved\n        \"\"\"",
        "self",
        ".",
        "model_dir",
        "=",
        "model_dir"
    ],
    "original_comment": "\"\"\"\n        :param model_dir: The path of the directory where models should be saved\n        \"\"\"",
    "comment": ":param model_dir: The path of the directory where models should be saved",
    "comment_tokens": [
        ":",
        "param",
        "model_dir",
        ":",
        "The",
        "path",
        "of",
        "the",
        "directory",
        "where",
        "models",
        "should",
        "be",
        "saved"
    ],
    "start_point": [
        1,
        8
    ],
    "end_point": [
        3,
        11
    ],
    "prev_context": {
        "code": null,
        "start_point": null,
        "end_point": null
    },
    "next_context": {
        "code": "self.model_dir = model_dir",
        "start_point": [
            4,
            8
        ],
        "end_point": [
            4,
            34
        ]
    }
}

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

Languages	Number of inline comments
Python	14,013,238
Java	17,062,277
JavaScript	1,438,110
PHP	5,873,744
C	6,778,239
C#	6,274,389
C++	10,343,650
Go	4,390,342
Ruby	767,563
Rust	2,063,784
TOTAL	69,005,336

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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