ronantakizawa/github-top-code

GitHub Top Developer Source Code

A curated dataset of 1.3M+ source code files from GitHub's top ranked developers (2015-2025).

This dataset is based on the top ranked developers from this dataset: https://huggingface.co/datasets/ronantakizawa/github-top-developers

Dataset Summary

• —1.3M+ source code files from repositories across ~4,700 unique developers
• —80+ programming languages included (Python, JavaScript, TypeScript, Rust, Go, C/C++, Java, and more)
• —Source code only — config files (JSON, YAML, TOML, etc.) and documentation (Markdown, TXT) are excluded
• —Permissive licenses only (MIT, Apache-2.0, BSD, ISC, etc.)
• —Rich metadata per file: repo stars, description, primary language, developer company affiliation

Schema

Each row represents a single source file:

Note on language columns: file_language is determined per-file from the file extension (e.g. a .py file is always Python). repo_primary_language is GitHub's auto-detected primary language for the entire repository. These may differ — for example, a C header file (.h → C/C++ Header) in a repo that GitHub classifies as Python.

Splits

Splits are assigned by repository (deterministic hash), so no repo appears in multiple splits. This prevents data leakage from files in the same project.

Usage

from datasets import load_dataset

# Load a specific split
train = load_dataset("ronantakizawa/github-top-code", split="train")
test = load_dataset("ronantakizawa/github-top-code", split="test")

# Filter by language
python_files = train.filter(lambda x: x["file_language"] == "Python")

# Filter by stars
popular = train.filter(lambda x: x["repo_stars"] > 1000)

# Get files from a specific developer
dev_files = train.filter(lambda x: x["developer_username"] == "torvalds")