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Fsoft-AIC
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the-vault-function

Tasks:
Text Generation
Languages:
code
English
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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
  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

logo

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

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 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": "",
  "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."
  "parameters": [],
  "docstring_params": {}
}

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): 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 (list): tokenized version of code
  • short_docstring (string): short, brief summarization (first line of the docstring)
  • short_docstring_tokens (list): tokenized version of `short_docstring
  • docstring (string): the top-level comment or docstring (docstring version without param’s doc, return, exception fields, etc)
  • docstring_tokens (list): tokenized version of docstring
  • comment (list): list of comments (line) inside the function/class
  • parameters (list): List of parameters and its type (type can be None)
  • docstring_params (dict): Dictionary of the parsed information from docstring

See here for more details and examples.

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 (approximate 20,000 samples in each). The statistic for each language is illustrated in the following section.

Before split, the dataset is de-duplicated with the test sets in CodeSearchNet, HumanEval, APPS, CoDesc. There are 3 versions of training set that are small (5%), medium (20%) and large (100%).

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", "full") version of function level training set
dataset = load_dataset("Fsoft-AIC/the-vault-function", split_set=["train/small"])

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

# dataset streaming
data = load_dataset("Fsoft-AIC/the-vault-function", split_set= ["train"])
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.