---
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.

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**⭐ 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."**