| | --- |
| | 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 |
| |
|
| | ```python |
| | # 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 |
| |
|
| | ```python |
| | { |
| | "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 |
| |
|
| | ```python |
| | 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."** |
| |
|
