README.md

---
dataset_name: "CodeReality-EvalSubset"
pretty_name: "CodeReality: Evaluation Subset - Deliberately Noisy Code Dataset"
tags:
  - code
  - software-engineering
  - robustness
  - noisy-dataset
  - evaluation-subset
  - research-dataset
  - code-understanding
size_categories:
  - 10GB<n<100GB
task_categories:
  - text-generation
  - text-classification
  - text-retrieval
  - fill-mask
  - other
language:
  - en
  - code
license: other
configs:
  - config_name: default
    data_files: "data_csv/*.csv"
---

# CodeReality: Evaluation Subset - Deliberately Noisy Code Dataset

![Dataset Status](https://img.shields.io/badge/status-complete-brightgreen)
![Size](https://img.shields.io/badge/size-19GB-orange)
![Repositories](https://img.shields.io/badge/repositories-2,049-red)
![Subset](https://img.shields.io/badge/type-evaluation_subset-purple)

## ⚠️ Important Limitations

> **⚠️ Not Enterprise-Ready**: This dataset is deliberately noisy and designed for research only. Contains mixed/unknown licenses, possible secrets, potential security vulnerabilities, duplicate code, and experimental repositories. **Requires substantial preprocessing for production use.**
>
> **Use at your own risk** - this is a research dataset for robustness testing and data curation method development.

## Overview

**CodeReality Evaluation Subset** is a curated research subset extracted from the complete CodeReality dataset (3.05TB, 397,475 repositories). This subset contains **2,049 repositories** in **19GB** of data, specifically selected for standardized evaluation and benchmarking of code understanding models on deliberately noisy data.
For complete Dataset 3tb, please contact me at vincenzo.gallo77@hotmail.com

### Key Features
- ✅ **Curated Selection**: Research value scoring with diversity sampling from 397,475 repositories
- ✅ **Research Grade**: Comprehensive analysis with transparent methodology
- ✅ **Deliberately Noisy**: Includes duplicates, incomplete code, and experimental projects
- ✅ **Rich Metadata**: Enhanced Blueprint metadata with cross-domain classification
- ✅ **Professional Grade**: 63.7-hour comprehensive analysis with open source tools

## Quick Start

### Dataset Structure
```
codereality-1t/
├── data_csv/               # Evaluation subset data (CSV format, 2,387 repositories)
│   ├── codereality_unified.csv  # Main dataset file with unified schema
│   └── metadata.json           # Dataset metadata and column information
├── analysis/               # Analysis results and tools
│   ├── dataset_index.json  # File index and metadata
│   └── metrics.json        # Analysis results
├── docs/                   # Documentation
│   ├── DATASET_CARD.md     # Comprehensive dataset card
│   └── LICENSE.md          # Licensing information
├── benchmarks/             # Benchmarking scripts and frameworks
├── results/                # Evaluation results and metrics
├── Notebook/               # Analysis notebooks and visualizations
├── eval_metadata.json      # Evaluation metadata and statistics
└── eval_subset_stats.json  # Statistical analysis of the subset
```

### Loading the Dataset

## 📊 **Unified CSV Format**

**This dataset has been converted to CSV format with a unified schema** to ensure compatibility with Hugging Face's dataset viewer and eliminate schema inconsistencies that were present in the original JSONL format.

### **How to Use This Dataset**

**Option 1: Standard Hugging Face Datasets (Recommended)**
```python
from datasets import load_dataset

# Load the complete dataset
dataset = load_dataset("vinsblack/CodeReality")

# Access the data
print(f"Total samples: {len(dataset['train'])}")
print(f"Columns: {dataset['train'].column_names}")

# Sample record
sample = dataset['train'][0]
print(f"Repository: {sample['repo_name']}")
print(f"Language: {sample['primary_language']}")
print(f"Quality Score: {sample['quality_score']}")
```

**Option 2: Direct CSV Access**
```python
import pandas as pd
from huggingface_hub import snapshot_download

# Download the dataset
repo_path = snapshot_download(repo_id="vinsblack/CodeReality", repo_type="dataset")

# Load CSV files
import glob
csv_files = glob.glob(f"{repo_path}/data_csv/*.csv")
df = pd.concat([pd.read_csv(f) for f in csv_files], ignore_index=True)

print(f"Total records: {len(df)}")
print(f"Columns: {list(df.columns)}")
```

**Option 3: Metadata and Analysis**
```python
# Load evaluation subset metadata
with open('eval_metadata.json', 'r') as f:
    metadata = json.load(f)

print(f"Subset: {metadata['eval_subset_info']['name']}")
print(f"Files: {metadata['subset_statistics']['total_files']}")
print(f"Repositories: {metadata['subset_statistics']['estimated_repositories']}")
print(f"Size: {metadata['subset_statistics']['total_size_gb']} GB")

# Access evaluation data files
data_dir = "data/"  # Local evaluation subset data
for filename in os.listdir(data_dir)[:5]:  # First 5 files
    file_path = os.path.join(data_dir, filename)
    with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:
        for line in f:
            repo_data = json.loads(line)
            print(f"Repository: {repo_data.get('name', 'Unknown')}")
            break  # Just first repo from each file
```

## Dataset Statistics

### Evaluation Subset Scale
- **Total Repositories**: 2,049 (curated from 397,475)
- **Total Files**: 323 JSONL archives
- **Total Size**: 19GB uncompressed
- **Languages Detected**: Multiple (JavaScript, Python, Java, C/C++, mixed)
- **Selection**: Research value scoring with diversity sampling
- **Source Dataset**: CodeReality complete dataset (3.05TB)

### Language Distribution (Top 10)
| Language | Repositories | Percentage |
|----------|-------------|------------|
| Unknown | 389,941 | 98.1% |
| Python | 4,738 | 1.2% |
| Shell | 4,505 | 1.1% |
| C | 3,969 | 1.0% |
| C++ | 3,339 | 0.8% |
| HTML | 2,487 | 0.6% |
| JavaScript | 2,394 | 0.6% |
| Go | 2,110 | 0.5% |
| Java | 2,026 | 0.5% |
| CSS | 1,655 | 0.4% |

### Duplicate Analysis
**Exact Duplicates**: 0% exact SHA256 duplicates detected across file-level content
**Semantic Duplicates**: ~18% estimated semantic duplicates and forks preserved by design
**Research Value**: Duplicates intentionally maintained for real-world code distribution studies

### License Analysis
**License Detection**: 0% detection rate (design decision for noisy dataset research)
**Unknown Licenses**: 96.4% of repositories marked as "Unknown" by design
**Research Purpose**: Preserved to test license detection systems and curation methods

### Security Analysis
⚠️ **Security Warning**: Dataset contains potential secrets
- Password patterns: 1,231,942 occurrences
- Token patterns: 353,266 occurrences
- Secret patterns: 71,778 occurrences
- API key patterns: 4,899 occurrences

## Research Applications

### Primary Use Cases
1. **Code LLM Robustness**: Testing model performance on noisy, real-world data
2. **Data Curation Research**: Developing automated filtering and cleaning methods
3. **License Detection**: Training and evaluating license classification systems
4. **Bug-Fix Studies**: Before/after commit analysis for automated debugging
5. **Cross-Language Analysis**: Multi-language repository understanding

### About This Evaluation Subset
This repository contains the **19GB evaluation subset** designed for standardized benchmarks:
- **323 files** containing **2,049 repositories**
- Research value scoring with diversity sampling
- Cross-language implementations and multi-repo analysis
- Complete build system configurations
- Enhanced metadata with commit history and issue tracking

**Note**: The complete 3.05TB CodeReality dataset with all 397,475 repositories is available separately. Contact vincenzo.gallo77@hotmail.com for access to the full dataset.

**Demonstration Benchmarks** available in `eval/benchmarks/`:
- **License Detection**: Automated license classification evaluation
- **Code Completion**: Pass@k metrics for code generation models
- **Extensible Framework**: Easy to add new evaluation tasks

## Benchmarks & Results

### 📊 **Baseline Performance**
Demonstration benchmark results available in `eval/results/`:
- [`license_detection_sample_results.json`](eval/results/license_detection_sample_results.json) - 9.8% accuracy (challenging baseline)
- [`code_completion_sample_results.json`](eval/results/code_completion_sample_results.json) - 14.2% Pass@1 (noisy data challenge)

### 🏃 **Quick Start Benchmarking**
```bash
cd eval/benchmarks
python3 license_detection_benchmark.py    # License classification
python3 code_completion_benchmark.py      # Code generation Pass@k
```

**Note**: These are demonstration baselines, not production-ready models. Results show expected challenges of deliberately noisy data.

### 📊 **Benchmarks & Results**
- **License Detection**: 9.8% accuracy baseline ([`license_detection_sample_results.json`](eval/results/license_detection_sample_results.json))
- **Code Completion**: 14.2% Pass@1, 34.6% Pass@5 ([`code_completion_sample_results.json`](eval/results/code_completion_sample_results.json))
- **Framework Scaffolds**: Bug detection and cross-language translation ready for community implementation
- **Complete Analysis**: [`benchmark_summary.csv`](eval/results/benchmark_summary.csv) - All metrics for easy comparison and research use

## Usage Guidelines

### ✅ Recommended Uses
- Academic research and education
- Robustness testing of code models
- Development of data curation methods
- License detection research
- Security pattern analysis

### ❌ Important Limitations
- **No Commercial Use** without individual license verification
- **Research Only**: Many repositories have unknown licensing
- **Security Risk**: Contains potential secrets and vulnerabilities
- **Deliberately Noisy**: Requires preprocessing for most applications

## ⚠️ Important: Dataset vs Evaluation Subset

**This repository contains the 19GB evaluation subset only.** Some files within this repository (such as `docs/DATASET_CARD.md`, notebooks in `Notebook/`, and analysis results) reference or describe the complete 3.05TB CodeReality dataset. This is intentional for research context and documentation completeness.

### What's in this repository:
- ✅ **Evaluation subset data**: 19GB, 2,049 repositories in `data/` directory
- ✅ **Analysis tools and scripts**: For working with both subset and full dataset
- ✅ **Documentation**: Describes both the subset and the complete dataset methodology
- ✅ **Benchmarks**: Ready to use with the evaluation subset

### Complete Dataset Access (3.05TB):
- 📧 **Contact**: vincenzo.gallo77@hotmail.com for access to the full dataset
- 📊 **Full Scale**: 397,475 repositories across 21 programming languages
- 🗂️ **Size**: 3.05TB uncompressed, 52,692 JSONL files

#### Who Should Use the Complete Dataset:
- 🎯 **Large-scale ML researchers** training foundation models on massive code corpora
- 🏢 **Enterprise teams** developing production code understanding systems
- 🔬 **Academic institutions** conducting comprehensive code analysis studies
- 📊 **Data scientists** performing statistical analysis on repository distributions
- 🛠️ **Tool developers** building large-scale code curation and filtering systems

#### Advantages of Complete Dataset vs Evaluation Subset:
| Feature | Evaluation Subset (19GB) | Complete Dataset (3.05TB) |
|---------|-------------------------|---------------------------|
| **Repositories** | 2,049 curated | 397,475 complete coverage |
| **Use Case** | Benchmarking & evaluation | Large-scale training & research |
| **Data Quality** | High (curated selection) | Mixed (deliberately noisy) |
| **Languages** | Multi-language focused | 21+ languages comprehensive |
| **Setup Time** | Immediate | Requires infrastructure planning |
| **Best For** | Model evaluation, testing | Model training, comprehensive analysis |

#### Choose Complete Dataset When:
- ✅ Training large language models requiring massive code corpora
- ✅ Developing data curation algorithms at scale
- ✅ Studying real-world code distribution patterns
- ✅ Building production-grade code understanding systems
- ✅ Researching cross-language programming patterns
- ✅ Creating comprehensive code quality metrics

#### Choose Evaluation Subset When:
- ✅ Benchmarking existing models
- ✅ Quick prototyping and testing
- ✅ Learning to work with noisy code datasets
- ✅ Limited storage or computational resources
- ✅ Focused evaluation on curated, high-value repositories

## Configuration Files (YAML)

The project includes comprehensive YAML configuration files for easy programmatic access:

| Configuration File | Description |
|-------------------|-------------|
| [`dataset-config.yaml`](dataset-config.yaml) | Main dataset metadata and structure |
| [`analysis-config.yaml`](analysis-config.yaml) | Analysis methodology and results |
| [`benchmarks-config.yaml`](benchmarks-config.yaml) | Benchmarking framework configuration |

### Using Configuration Files

```python
import yaml

# Load dataset configuration
with open('dataset-config.yaml', 'r') as f:
    dataset_config = yaml.safe_load(f)

print(f"Dataset: {dataset_config['dataset']['name']}")
print(f"Version: {dataset_config['dataset']['version']}")
print(f"Total repositories: {dataset_config['dataset']['metadata']['total_repositories']}")

# Load analysis configuration
with open('analysis-config.yaml', 'r') as f:
    analysis_config = yaml.safe_load(f)

print(f"Analysis time: {analysis_config['analysis']['methodology']['total_time_hours']} hours")
print(f"Coverage: {analysis_config['analysis']['methodology']['coverage_percentage']}%")

# Load benchmarks configuration
with open('benchmarks-config.yaml', 'r') as f:
    benchmarks_config = yaml.safe_load(f)

for benchmark in benchmarks_config['benchmarks']['available_benchmarks']:
    print(f"Benchmark: {benchmark}")
```

## Documentation

| Document | Description |
|----------|-------------|
| [Dataset Card](docs/DATASET_CARD.md) | Comprehensive dataset documentation |
| [License](docs/LICENSE.md) | Licensing terms and legal considerations |
| [Data README](data/README.md) | Data access and usage instructions |

## Verification

Verify dataset integrity:
```bash
# Check evaluation subset counts
python3 -c "
import json
with open('eval_metadata.json', 'r') as f:
    metadata = json.load(f)
    print(f'Files: {metadata[\"subset_statistics\"][\"total_files\"]}')
    print(f'Repositories: {metadata[\"subset_statistics\"][\"estimated_repositories\"]}')
    print(f'Size: {metadata[\"subset_statistics\"][\"total_size_gb\"]} GB')
"

# Expected output:
# Files: 323
# Repositories: 2049
# Size: 19.0 GB
```

## Citation

```bibtex
@misc{codereality2025,
  title={CodeReality Evaluation Subset: A Curated Research Dataset for Robust Code Understanding},
  author={Vincenzo Gallo},
  year={2025},
  note={Version 1.0.0 - Evaluation Subset (19GB from 3.05TB source)}
}
```

## Community Contributions

We welcome community contributions to improve CodeReality-1T:

### 🛠️ **Data Curation Scripts**
- Contribute filtering and cleaning scripts for the noisy dataset
- Share deduplication algorithms and quality improvement tools
- Submit license detection and classification improvements

### 📊 **New Benchmarks**
- Add evaluation tasks beyond license detection and code completion
- Contribute cross-language analysis benchmarks
- Share bug detection and security analysis evaluations

### 📈 **Future Versions**
- **v1.1.0**: Enhanced evaluation subset with community feedback
- **v1.2.0**: Improved license detection and filtering tools
- **v2.0.0**: Community-curated clean variant with quality filters

### 🤝 **How to Contribute**
**Community contributions are actively welcomed and encouraged!** Help improve the largest deliberately noisy code dataset.

**🎯 Priority Contribution Areas**:
- **Data Curation**: Cleaning scripts, deduplication algorithms, quality filters
- **Benchmarks**: New evaluation tasks, improved baselines, framework implementations
- **Analysis Tools**: Visualization, statistics, metadata enhancement
- **Documentation**: Usage examples, tutorials, case studies

**📋 Contribution Process**:
1. Clone the repository locally
2. Review existing analysis in the `analysis/` directory
3. Develop improvements or new features
4. Test your contributions thoroughly
5. Submit your improvements via standard collaboration methods

**💡 Join the Community**: Share your research, tools, and insights using CodeReality!

## Support & Access

### Evaluation Subset (This Repository)
- **Documentation**: See `docs/` directory for comprehensive information
- **Analysis**: Check `analysis/` directory for current research insights
- **Usage**: All benchmarks and tools work directly with the 19GB subset

### Complete Dataset Access (3.05TB)
- **🔗 Full Dataset Request**: Contact vincenzo.gallo77@hotmail.com
- **📋 Include in your request**:
  - Research purpose and intended use
  - Institutional affiliation (if applicable)
  - Technical requirements and storage capacity
- **⚡ Response time**: Typically within 24-48 hours

### General Support
- **Technical Questions**: vincenzo.gallo77@hotmail.com
- **Documentation Issues**: Check `docs/` directory first
- **Benchmark Problems**: Review `benchmarks/` and `results/` directories

---

*Dataset created using transparent research methodology with complete reproducibility. Analysis completed in 63.7 hours with 100% coverage and no sampling.*