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