read

README.md

@@ -1,3 +1,126 @@
----
-license: mit
----
+---
+license: mit
+task_categories:
+- text-generation
+- text-classification
+- question-answering
+language:
+- en
+tags:
+- code
+size_categories:
+- 10K<n<100K
+---
+---
+license: mit
+task_categories:
+- text-generation
+- text-classification
+- question-answering
+language:
+- en
+tags:
+- code
+size_categories:
+- 10K<n<100K
+---
+# PyHub: Vetted Python Code from Popular GitHub Repositories
+
+[![Python](https://img.shields.io/badge/Python-3.8%2B-blue)](https://www.python.org/)
+[![License](https://img.shields.io/badge/License-MIT-green)](LICENSE)
+
+A large-scale dataset of Python source code, test files, and documentation scraped from high-quality GitHub repositories. Designed for training code understanding and generation models, particularly for software engineering benchmarks like SWE-bench.
+
+## Dataset Statistics
+
+- **Total files**: 271,995
+- **Repositories**: 50+ (minimum 50 stars)
+- **File types**: Python source, test files, READMEs
+- **Time period**: Repositories created before January 1, 2020
+- **Size limit**: Maximum 100 MB per repository
+- **License**: MIT
+
+## Dataset Structure
+
+Each row in `dataset.csv` represents a single file with the following columns:
+
+| Column | Description |
+|--------|-------------|
+| `repo_name` | Repository name (e.g., "requests") |
+| `repo_full_name` | Full repository name (e.g., "psf/requests") |
+| `owner` | Repository owner (e.g., "psf") |
+| `stars` | Star count |
+| `license` | SPDX license identifier |
+| `repo_description` | Repository description |
+| `filepath` | Relative path within repository |
+| `file_type` | "python", "test", or "readme" |
+| `language` | "Python", "Markdown", "reStructuredText", or "" |
+| `content` | Full file text |
+| `size_bytes` | File size in bytes |
+| `num_lines` | Number of lines |
+
+## File Types
+
+- **Python source** (`.py`): Production code files
+- **Test files** (`*test*.py`): Unit tests and test suites
+- **README files** (`README.*`): Documentation in Markdown, reStructuredText, or plain text
+
+## Collection Methodology
+
+The dataset was collected using a custom GitHub scraper with the following process:
+
+1. **Repository selection**: GitHub API search for repositories with x ≥ 50 stars, created before 2020-01-01, non-fork
+2. **Cloning**: Shallow git clone (`--depth 1`) with 100 MB size filter to exclude large monorepos
+3. **File collection**: Recursive walk through cloned repositories, excluding hidden directories (files starting with `.`)
+4. **File type filtering**: Only Python source files (`.py`), test files (`*test*.py`), and README files (`README.*`) were collected
+5. **Content extraction**: UTF-8 encoding with error handling for robust text extraction
+6. **Parallel processing**: 3 concurrent workers for efficient processing
+7. **CSV generation**: All file data consolidated into a single CSV with repository metadata embedded in each row
+
+## Quality Filters
+
+- **Star threshold**: Minimum 50 stars (indicates community vetting)
+- **Size limit**: 100 MB to exclude monorepos and binary-heavy projects
+- **File type filtering**: Only Python, test, and documentation files
+- **Hidden files excluded**: Files/directories starting with `.` ignored
+- **Encoding handling**: UTF-8 with error fallback
+
+## Intended Use Cases
+
+- **Code completion**: Training autocompletion models on real-world Python patterns
+- **Bug detection**: Learning from production codebases with established testing practices
+- **Test generation**: Understanding test-code relationships from included test files
+- **Documentation generation**: Learning code-documentation correlations from READMEs
+- **SWE-bench training**: Base dataset for software engineering benchmark preparation
+- **Code understanding**: Repository structure and dependency learning
+
+## Limitations
+
+- **Temporal bias**: Pre-2020 code, missing modern Python features (type hints, match statements, structural pattern matching)
+- **Popularity bias**: High-star repos only, may not represent niche or edge-case patterns
+- **Size limitation**: 100 MB cap excludes large enterprise monorepos
+- **Language bias**: Primarily English documentation and comments
+- **Static only**: No execution data, test results, or runtime behavior
+
+## Recommended Supplements
+
+For comprehensive model training, consider supplementing with:
+- Post-2020 repositories for modern Python patterns
+- Smaller repositories for edge-case and niche patterns
+- Synthetic examples for specific bug types
+- Negative examples (buggy code) for robustness
+
+## License
+
+This dataset is licensed under the MIT License. See the LICENSE file for details.
+
+## Contact
+
+furkannar168@hotmail.com
+
+or you can simply open up an issue for issues or questions that you'd like to adress or ask.
+
+---
+
+**Note**: This dataset was created using a custom GitHub scraper tool.
+