| | --- |
| | license: mit |
| | task_categories: |
| | - text-generation |
| | language: |
| | - code |
| | tags: |
| | - code |
| | - github |
| | - source-code |
| | - trending-developers |
| | - software-engineering |
| | size_categories: |
| | - 1M<n<10M |
| | --- |
| | |
| | # GitHub Top Developer Source Code |
| |
|
| | A curated dataset of **1.3M+ source code files** from **GitHub's ranked developers (2015-2025)**. |
| |
|
| | This dataset is filtered to code written by developers who repeatedly appeared on GitHub's trending page. |
| |
|
| | ## 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 |
| |
|
| | ## Use Cases |
| | |
| | - Code generation fine-tuning — Train or fine-tune LLMs on high-quality source code written by recognized developers, providing a higher signal-to-noise ratio than bulk GitHub scrapes |
| | - Cross-language code analysis — Study patterns across 80+ programming languages from the same pool of elite developers to compare idioms, styles, and conventions |
| | - Open source contribution patterns — Analyze how prolific open source contributors structure their most popular projects |
| | |
| |
|
| | ## Schema |
| |
|
| | Each row represents a single source file: |
| |
|
| | | Column | Type | Description | |
| | |--------|------|-------------| |
| | | `file_path` | string | Path within the repo (e.g. `src/main.py`) | |
| | | `file_language` | string | Language detected from file extension (e.g. `Python`, `JavaScript`) | |
| | | `content` | string | Raw source code (UTF-8) | |
| | | `repo_name` | string | Full repository name (`owner/repo`) | |
| | | `repo_stars` | int64 | GitHub star count at time of collection | |
| | | `repo_description` | string | Repository description | |
| | | `repo_primary_language` | string | GitHub-detected primary language of the repository | |
| | | `developer_username` | string | GitHub username | |
| | | `developer_name` | string | Developer display name | |
| | | `developer_company` | string | Company affiliation | |
| |
|
| | **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`. |
| |
|
| | ## Usage |
| |
|
| | ```python |
| | from datasets import load_dataset |
| | |
| | ds = load_dataset("ronantakizawa/github-top-code", split="train") |
| | |
| | # Filter by language |
| | python_files = ds.filter(lambda x: x["file_language"] == "Python") |
| | |
| | # Filter by stars |
| | popular = ds.filter(lambda x: x["repo_stars"] > 1000) |
| | |
| | # Get files from a specific developer |
| | dev_files = ds.filter(lambda x: x["developer_username"] == "torvalds") |
| | ``` |
| |
|
| |
|
