Gugu8/Code-Syntax
Code Syntax Dataset (S) A large-scale, high‑quality dataset for teaching large language models to identify and correct common syntax errors across 30+ programming languages.Contains 500,000+ unique examples (≈110 MB) with English explanations – no artificial padding. 📊 Dataset Format The dataset is provided as a single CSV file with the following columns: Column Type Description wrong_code string Code snippet containing a syntax error correct_code… See the full description on the dataset page: https://huggingface.co/datasets/Gugu8/Code-Syntax.
Code Syntax Dataset (S)
A large-scale, high‑quality dataset for teaching large language models to identify and correct common syntax errors across 30+ programming languages. Contains 500,000+ unique examples (≈110 MB) with English explanations – no artificial padding.
📊 Dataset Format
The dataset is provided as a single CSV file with the following columns:
🧠 Languages Covered
The dataset includes examples from 30+ languages and technologies:
💡 Example Entries
Here are a few sample rows to illustrate the dataset content:
Each example is unique – variable names, numbers, and string values are randomised, ensuring a wide variety of patterns for robust model training.
🎯 Use Cases
- Fine‑tuning LLMs – train models to correct erroneous code or to generate correct code from buggy input.
- Building code‑review assistants – create tools that automatically detect and suggest fixes for common syntax mistakes.
- Educational materials – use the dataset as a large, searchable bank of common programming pitfalls.
- Benchmarking – evaluate how well models understand language‑specific syntax rules.
📈 Dataset Statistics
📄 License
This dataset is released under the Open Metadata License (OpenMDW v1.1) – you are free to use, modify, and distribute it for any purpose, subject to the terms of that license.
🙋 Contributions & Feedback
If you have suggestions for additional languages, error patterns, or improvements, feel free to reach out or open an issue. We welcome contributions to make this dataset even more comprehensive.
Happy training! 🚀
