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
splits:
- name: python
num_bytes: 30797754227
num_examples: 9893858
- name: java
num_bytes: 23130202517
num_examples: 7886299
- name: javascript
num_bytes: 6833869001
num_examples: 2562158
- name: php
num_bytes: 13072500520
num_examples: 5455989
- name: c_sharp
num_bytes: 11144245789
num_examples: 4011467
- name: c
num_bytes: 6205820571
num_examples: 1978551
- name: cpp
num_bytes: 6228306797
num_examples: 1934958
- name: go
num_bytes: 11339059495
num_examples: 5649158
- name: rust
num_bytes: 2661037428
num_examples: 1076588
- name: ruby
num_bytes: 1224195690
num_examples: 544867
download_size: 26404353470
dataset_size: 112636992035
pretty_name: The Vault
viewer: true
Table of Contents
- Dataset Description
- Dataset Summary
- Supported Tasks
- Languages
- Dataset Structure
- Dataset Statistics
- Usage
- Additional Information
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/
Dataset Summary
The Vault is a multilingual code-text dataset with over 40 million pairs 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 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
{
"repo": "irshadbhat/sndpcs",
"path": "arc_eager.py",
"license": "MIT"
"identifier": "REDUCE",
"return_type": "<not_specify>"
"language": "Python",
"code": "def REDUCE(self, configuration, label=None):\n b0 = configuration.b0\n configuration.stack.pop()",
"code_tokens": "def REDUCE ( self , configuration , label = None ) : b0 = configuration . b0 configuration . stack . pop ( )",
"original_docstring": "\n pops the top of the stack if it has got its head.\n ",
"comment": "\"\"\"\n pops the top of the stack if it has got its head.\n \"\"\"",
"docstring_tokens": "pops the top of the stack if it has got its head .",
"docstring": "pops the top of the stack if it has got its head."
}
Data Fields
Data fields for function level:
- repo (string): the owner/repo
- path (string): the full path to the original file
- license (string): license in the repo
- language (string): the programming language
- identifier (string): the function or method name
- return_type (string): the type returned by the function
- original_string (string): original version of function/class node
- original_docstring (string): the raw string before tokenization or parsing
- code (string): the part of the original that is code
- code_tokens (string): tokenized version of
code
, separated by whitespace - short_docstring (string): short, brief summarization (first line of the docstring)
- short_docstring_tokens (string): tokenized version of
short_docstring
, separated by whitespace - docstring (string): the top-level comment or docstring (docstring version without param’s doc, return, exception, etc)
- docstring_tokens tokenized version of docstring, separated by whitespace
- comment (string): comment (line) inside the function/class, separated by
$SEP$
token - parameters (dict): Dictionary of parameters and its type (type can be None)
- docstring_params (dict): Dictionary of the parsed information from docstring
Due to the limitation of the huggingface data structure, we do not contain parameters and docstring_params fields in this repo. The detail of data fields can be found in The Vault data format and the full dataset version can be downloaded here.
Data Splits
In this repo, The Vault is divided into 5 subsets, where three training versions are split based on dataset size, and the remains are validation set and test set. The statistic for each language is illustrated in the following section.
Dataset Statistics
- Compare to other benchmarks
Dataset | #Language | #Code-text pair |
---|---|---|
PyMT5 | 1 | ≈ 7,700,000 |
CoDesc | 1 | 4,211,516 |
CodeSearchNet | 6 | 2,326,976 |
CodeSearchNet (CodeXGLUE) | 6 | 1,005,474 |
Deepcom | 1 | 424,028 |
CONCODE | 1 | 2,184,310 |
Funcom | 1 | 2,149,121 |
CodeT5 | 8 | 3,158,313 |
The Vault | 10 | 40,993,893 |
- Statistic for each language in The Vault
Language | #Code-text pair | #Repository |
---|---|---|
Python | 9,893,858 | 628,069 |
PHP | 5,455,989 | 439,514 |
JavaScript | 2,562,158 | 355,761 |
Java | 7,886,299 | 321,129 |
C# | 4,011,467 | 150,657 |
C++ | 1,934,958 | 116,897 |
C | 1,978,551 | 88,556 |
Go | 5,649,158 | 241,238 |
Rust | 1,076,588 | 68,615 |
Ruby | 544,867 | 61,804 |
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-function")
# Load function level train/validation/test set
dataset = load_dataset("Fsoft-AIC/the-vault-function", split_set=["train"])
# Load "small" (or "medium", "large") function level training set
dataset = load_dataset("Fsoft-AIC/the-vault-function", split_set=["train/small"])
# specific language (e.g. Golang)
dataset = load_dataset("Fsoft-AIC/the-vault-function", split_set=["train"], languages=['Go'])
Additional information
Licensing Information
[More information needed]
Citation Information
@misc{manh2023vault,
title={The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation},
author={Dung Nguyen Manh and Nam Le Hai and Anh T. V. Dau and Anh Minh Nguyen and Khanh Nghiem and Jin Guo and Nghi D. Q. Bui},
year={2023},
}
Contributions
This dataset is developed by FSOFT AI4Code team.