🚀 Explore SLAB™ in Action: Visit SLAB Protocol Website
SLAB™ is an open data standard designed to bring structure and consistency to the trading card industry. It leverages AI-powered data extraction to transform unstructured card descriptions into machine-readable data.
- The trading card market lacks a universal data standard.
- Descriptions vary across platforms, making it hard to search, verify, price, and manage cards.
- SLAB™ introduces a standardized format to simplify inventory management, marketplace listings, and data sharing.
The SLAB™ front-end app helps users:
- Upload raw card data (e.g., descriptions).
- Automatically extract structured fields (Year, Player, Set, Parallel, etc.) using AI.
- Review, refine, and export SLAB™-compliant data (including XML).
- Contribute validated data to improve the AI models.
After extensive testing of different approaches, SLAB™ found that combining ChatGPT's natural language understanding with a custom-trained extraction model is the most effective solution for parsing trading card descriptions. Here's why:
- Flexibility: ChatGPT excels at interpreting new and varied descriptions, handling edge cases better than rigid rule-based systems.
- Domain-Specific Precision: The custom model is trained specifically on trading card data, allowing it to accurately identify structured fields like Player Name, Set, and Parallel.
- Iterative Improvement: The hybrid approach allows SLAB™ to leverage GPT's reasoning capabilities when the custom model encounters uncertainty, improving overall accuracy.
- Scalability: This approach scales well as the dataset is continuously expanded and the model is refined with community contributions.
Other methods explored—like regex-only parsing, rule-based systems, and standalone machine learning models—proved to be:
- Too brittle: They often failed when descriptions deviated from expected patterns.
- Maintenance-heavy: Required constant updates as new sets and parallels were released.
- Lacking adaptability: Struggled to handle the diverse and evolving nature of trading card descriptions.
Combining GPT’s language understanding with the specialized extraction model delivers the best balance of accuracy, adaptability, and scalability.
- This is an early test version built with Streamlit; some quirks are expected.
- Focused on establishing the foundation for standardization, continually improving the model and learning AI along the way!
Feedback is welcome! Reach out at:
slabprotocolfeedback@gmail.com