license: mit
modalities:
- Text
formats:
- parquet
size: 10M - 100M
libraries:
- Datasets
- Dask
- Croissant
- Polars
🚀 GitHub Code 2025: The Clean Code Manifesto
A meticulously curated dataset of 1.5M+ repositories representing both quality and innovation in 2025's code ecosystem
🌟 The Philosophy
Quality Over Quantity, Purpose Over Volume
In an era of data abundance, we present a dataset built on radical curation. Every file, every repository, every byte has been carefully selected to represent the signal in the noise of open-source development.
🎯 What This Dataset Is
📊 Dual-Perspective Design
| Subset | 🎖️ Above 2 Stars | 🌱 Below 2 Stars (2025) |
|---|---|---|
| Scope | 1M top repositories | 1M random 2025 repos |
| Purpose | Proven quality & patterns | Emerging trends & innovation |
| Value | What works | What's next |
🧹 The Clean Code Promise
# What you WON'T find here:
🚫 Binary files # No images, executables, models
🚫 Build artifacts # No node_modules, __pycache__
🚫 Configuration noise # No .git, IDE files, lock files
🚫 License duplication # No repetitive legal text
🚫 Minified code # No compressed/obfuscated content
🚫 Empty files # No whitespace-only content
📁 Dataset Structure
github-code-2025/
├── 📈 above-2-stars/
│ ├── train_000.parquet
│ ├── train_001.parquet
│ └── ...
└── 🌱 below-2-star/
├── train_000.parquet
├── train_001.parquet
└── ...
📊 Schema
{
"repo_id": "owner/repo_name", # 📍 Repository identifier
"file_path": "src/main.py", # 🗂️ Relative file path
"content": "def clean_code():", # 💎 Actual source code
"size": 1024 # 📏 File size in bytes
}
🛠️ How to Use
🔥 Quick Start
from datasets import load_dataset
# Load the quality benchmark
quality_ds = load_dataset("nick007x/github-code-2025", "above-2-stars")
# Load emerging trends
emerging_ds = load_dataset("nick007x/github-code-2025", "below-2-star")
# Mix for balanced training
balanced_ds = interleave_datasets([quality_ds, emerging_ds])
🎯 Ideal Use Cases
- 🧠 AI Training: Clean, diverse code for language models
- 📊 Code Analysis: Compare popular vs emerging patterns
- 🔍 Trend Research: 2025 development practices
- 🎓 Education: High-quality examples for learning
- 🛠️ Tool Development: Benchmarking code quality tools
🏗️ Creation Methodology
🎨 Selection Strategy
| Phase | Action | Purpose |
|---|---|---|
| 1 | 🎯 Dual population sampling | Balance quality & innovation |
| 2 | 🧹 Multi-layer filtering | Remove noise & binaries |
| 3 | 📏 Size normalization | Focus on meaningful content |
| 4 | 🔍 Content validation | Ensure text quality |
| 5 | 🏷️ Metadata preservation | Maintain context |
🚫 What We Filtered Out
File Types Removed:
- 50+ binary extensions (images, models, executables)
- 30+ build/system directories
- 15+ configuration file types
- All files outside 1KB-5MB range
Quality Checks:
- ✅ UTF-8 text validation
- ✅ Non-empty content check
- ✅ Binary detection
- ✅ Repository structure preservation
🎪 Why This Dataset Matters
💫 The Quality Revolution
We reject the "more data is better" dogma. Instead, we offer:
- 🎯 Intentional Curation: Every file serves a purpose
- ⚖️ Balanced Perspective: Popular + Emerging = Complete picture
- 🧹 Unprecedented Cleanliness: The cleanest code dataset available
- 📅 Temporal Intelligence: 2025-focused for relevance
🤝 Contributing & Feedback
This dataset is a living project. We welcome:
- 🐛 Bug reports and issues
- 💡 Feature requests for future versions
- 📊 Validation of data quality
- 🎯 Suggestions for improvement
📜 License
This dataset is provided under the MIT License - see the LICENSE file for details.
Important: Repository contents maintain their original licenses. Please respect individual project licenses when using this data.
🙏 Acknowledgments
Built with gratitude for the entire open-source community. Every file in this dataset represents hours of dedication from developers worldwide.
⭐ If this dataset helps your research or project, please consider starring the repository!
"In the pursuit of AI that understands code, we must first understand what code is worth learning."