codeparrot/github-code

GitHub Code Dataset

Dataset Description

The GitHub Code dataset consists of 115M code files from GitHub in 32 programming languages with 60 extensions totaling in 1TB of data. The dataset was created from the public GitHub dataset on Google BiqQuery.

How to use it

The GitHub Code dataset is a very large dataset so for most use cases it is recommended to make use of the streaming API of datasets. You can load and iterate through the dataset with the following two lines of code:

from datasets import load_dataset

ds = load_dataset("codeparrot/github-code", streaming=True, split="train")
print(next(iter(ds)))

#OUTPUT:
{
 'code': "import mod189 from './mod189';\nvar value=mod189+1;\nexport default value;\n",
 'repo_name': 'MirekSz/webpack-es6-ts',
 'path': 'app/mods/mod190.js',
 'language': 'JavaScript',
 'license': 'isc',
 'size': 73
}

You can see that besides the code, repo name, and path also the programming language, license, and the size of the file are part of the dataset. You can also filter the dataset for any subset of the 30 included languages (see the full list below) in the dataset. Just pass the list of languages as a list. E.g. if your dream is to build a Codex model for Dockerfiles use the following configuration:

ds = load_dataset("codeparrot/github-code", streaming=True, split="train", languages=["Dockerfile"])
print(next(iter(ds))["code"])

#OUTPUT:
"""\
FROM rockyluke/ubuntu:precise

ENV DEBIAN_FRONTEND="noninteractive" \
    TZ="Europe/Amsterdam"
...
"""

We also have access to the license of the origin repo of a file so we can filter for licenses in the same way we filtered for languages:

ds = load_dataset("codeparrot/github-code", streaming=True, split="train", licenses=["mit", "isc"])

licenses = []
for element in iter(ds).take(10_000):
    licenses.append(element["license"])
print(Counter(licenses))

#OUTPUT:
Counter({'mit': 9896, 'isc': 104})

Naturally, you can also download the full dataset. Note that this will download ~300GB compressed text data and the uncompressed dataset will take up ~1TB of storage:

ds = load_dataset("codeparrot/github-code", split="train")

Data Structure

Data Instances

{
 'code': "import mod189 from './mod189';\nvar value=mod189+1;\nexport default value;\n",
 'repo_name': 'MirekSz/webpack-es6-ts',
 'path': 'app/mods/mod190.js',
 'language': 'JavaScript',
 'license': 'isc',
 'size': 73
}

Data Fields

Field	Type	Description
code	string	content of source file
repo_name	string	name of the GitHub repository
path	string	path of file in GitHub repository
language	string	programming language as inferred by extension
license	string	license of GitHub repository
size	int	size of source file in bytes

Data Splits

The dataset only contains a train split.

Languages

The dataset contains 30 programming languages with over 60 extensions:

{
    "Assembly": [".asm"],
    "Batchfile": [".bat", ".cmd"],
    "C": [".c", ".h"],
    "C#": [".cs"],
    "C++": [".cpp", ".hpp", ".c++", ".h++", ".cc", ".hh", ".C", ".H"],
    "CMake": [".cmake"],
    "CSS": [".css"],
    "Dockerfile": [".dockerfile", "Dockerfile"],
    "FORTRAN": ['.f90', '.f', '.f03', '.f08', '.f77', '.f95', '.for', '.fpp'],
    "GO": [".go"],
    "Haskell": [".hs"],
    "HTML":[".html"],
    "Java": [".java"],
    "JavaScript": [".js"],
    "Julia": [".jl"],
    "Lua": [".lua"],
    "Makefile": ["Makefile"],
    "Markdown": [".md", ".markdown"],
    "PHP": [".php", ".php3", ".php4", ".php5", ".phps", ".phpt"],
    "Perl": [".pl", ".pm", ".pod", ".perl"],
    "PowerShell": ['.ps1', '.psd1', '.psm1'],
    "Python": [".py"],
    "Ruby": [".rb"],
    "Rust": [".rs"],
    "SQL": [".sql"],
    "Scala": [".scala"],
    "Shell": [".sh", ".bash", ".command", ".zsh"],
    "TypeScript": [".ts", ".tsx"],
    "TeX": [".tex"],
    "Visual Basic": [".vb"]
}

Licenses

Each example is also annotated with the license of the associated repository. There are in total 15 licenses:

[
  'mit',
  'apache-2.0',
  'gpl-3.0',
  'gpl-2.0',
  'bsd-3-clause',
  'agpl-3.0',
  'lgpl-3.0',
  'lgpl-2.1',
  'bsd-2-clause',
  'cc0-1.0',
  'epl-1.0',
  'mpl-2.0',
  'unlicense',
  'isc',
  'artistic-2.0'
 ]

Dataset Statistics

The dataset contains 115M files and the sum of all the source code file sizes is 873 GB (note that the size of the dataset is larger due to the extra fields). A breakdown per language is given in the plot and table below:

	Language	File Count	Size (GB)
0	Java	19548190	107.70
1	C	14143113	183.83
2	JavaScript	11839883	87.82
3	HTML	11178557	118.12
4	PHP	11177610	61.41
5	Markdown	8464626	23.09
6	C++	7380520	87.73
7	Python	7226626	52.03
8	C#	6811652	36.83
9	Ruby	4473331	10.95
10	GO	2265436	19.28
11	TypeScript	1940406	24.59
12	CSS	1734406	22.67
13	Shell	1385648	3.01
14	Scala	835755	3.87
15	Makefile	679430	2.92
16	SQL	656671	5.67
17	Lua	578554	2.81
18	Perl	497949	4.70
19	Dockerfile	366505	0.71
20	Haskell	340623	1.85
21	Rust	322431	2.68
22	TeX	251015	2.15
23	Batchfile	236945	0.70
24	CMake	175282	0.54
25	Visual Basic	155652	1.91
26	FORTRAN	142038	1.62
27	PowerShell	136846	0.69
28	Assembly	82905	0.78
29	Julia	58317	0.29

Dataset Creation

The dataset was created in two steps:

1. Files of with the extensions given in the list above were retrieved from the GitHub dataset on BigQuery (full query here). The query was executed on Mar 16, 2022, 6:23:39 PM UTC+1.
2. Files with lines longer than 1000 characters and duplicates (exact duplicates ignoring whitespaces) were dropped (full preprocessing script here).

Considerations for Using the Data

The dataset consists of source code from a wide range of repositories. As such they can potentially include harmful or biased code as well as sensitive information like passwords or usernames.

Releases

You can load any older version of the dataset with the revision argument:

ds = load_dataset("codeparrot/github-code", revision="v1.0")

v1.0

• Initial release of dataset
• The query was executed on Feb 14, 2022, 12:03:16 PM UTC+1

v1.1

• Fix missing Scala/TypeScript
• Fix deduplication issue with inconsistent Python hash
• The query was executed on Mar 16, 2022, 6:23:39 PM UTC+1