GambitFlow/Elite-Data

📚 GambitFlow Elite Training Data

View on GitHub • Source Model: Nexus-core CE

📖 Dataset Description

This dataset is the highly curated input required to train strong, club-level chess evaluation models like the Nexus-core CE. It is designed to maximize the signal-to-noise ratio in chess data by removing moves made by lower-rated players.

By exclusively training on Elite-level games, the resulting AI avoids learning common amateur mistakes and focuses on solid positional principles.

🛠️ Data Engineering & Filtering

The database was created through a multi-stage, streaming pipeline to handle the massive volume efficiently without memory overflow.

1. Source: Lichess Public Database (January 2017).
2. CRITICAL FILTER: Only games where White ELO > 2000 AND Black ELO > 2000 were accepted.
3. Extraction: Positions (FENs) were extracted only up to the first 20 moves of each filtered game (the Opening/Early Middlegame phase).
4. Optimization: The data was aggregated by unique FEN and stored in a compressed SQLite file.

• Final Volume: Over 5,000,000 Total Positions processed, resulting in 2,488,753 Unique Positions.
• File Size: 882 MB.

📂 File Structure & Schema

The main file is chess_stats_v2.db.

Table: positions

Column	Type	Description
fen	TEXT (Primary Key)	The board position. Truncated to 4 parts (Position, Turn, Castling, En Passant) for maximum data aggregation across transpositions.
stats	TEXT (JSON)	JSON string containing aggregated move counts and game outcomes (W/D/L) for subsequent training.

🚀 Usage (Model Training)

This database is meant to be read by the SQLiteIterableDataset class in PyTorch, ensuring only small batches of data are streamed at a time, preventing RAM crashes even with large datasets.

⚠️ License

This dataset is licensed under CC BY-NC 4.0. It is a derivative work of the Lichess Open Database (CC0). Commercial use is strictly prohibited.

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Curated by GambitFlow