| --- |
| license: cc0-1.0 |
| task_categories: |
| - reinforcement-learning |
| - tabular-classification |
| language: |
| - en |
| tags: |
| - chess |
| - gambitflow |
| - synapse-base |
| - nexus-core |
| - elite |
| - sqlite |
| - big-data |
| size_categories: |
| - 10M<n<100M |
| pretty_name: GambitFlow Elite Training Data (Unified) |
| --- |
| |
| # 📚 GambitFlow Elite Training Data (Unified) |
|
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| <div align="center"> |
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| &descAlignY=60) |
| [](http://creativecommons.org/publicdomain/zero/1.0/) |
|  |
|  |
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| [**View on GitHub**](https://github.com/GambitFlow/GambitFlow) • [**Target Models: Nexus-Core & Synapse-Base**](https://huggingface.co/GambitFlow) |
|
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| </div> |
|
|
| ## 📖 Dataset Overview |
|
|
| This repository hosts the **foundational knowledge bases** for the GambitFlow chess engines. It consolidates two distinct, powerful datasets: |
|
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| 1. **`chess_stats_v2.db`**: The original, large-scale dataset used to train the **Nexus-Core** engine. |
| 2. **`match_positions_v2.db`**: A new, ultra-high-quality dataset specifically curated for the next-generation **Synapse-Base** engine. |
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| Together, they provide a comprehensive training resource covering different eras of chess theory and rating levels. |
|
|
| --- |
|
|
| ## 💎 Dataset 1: Synapse-Base Match Data (`match_positions_v2.db`) |
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|
| This is the **newly added**, highly-focused dataset designed to teach **Synapse-Base** advanced middlegame strategy and endgame technique. It prioritizes quality over quantity. |
|
|
| ### Data Engineering & Filtering |
| * **Source:** Lichess Elite Database (2024-2025 monthly archives). |
| * **Critical Filters:** |
| * **Player Rating:** Both players must have an ELO of **2400 or higher**. |
| * **Game Phase:** Skips the first 10 moves of every game to focus on non-theoretical positions. |
| * **Position Selection:** An intelligent filtering algorithm was used to select only "interesting" positions (e.g., positions with material imbalance, tactical complexity, or critical endgame structures). |
| * **Final Volume:** A dense collection of approximately **10,000,000** strategically rich positions. |
|
|
| ### Schema: `positions` table |
| | Column | Type | Description | |
| |--------|------|-------------| |
| | `fen` | TEXT | The board position (FEN). | |
| | `phase` | TEXT | 'midgame' or 'endgame'. | |
| | `value_target` | REAL | The game's outcome scored from -1.0 (loss) to 1.0 (win) from the current player's perspective. | |
| | `move_played` | TEXT | The move played by the 2400+ ELO human in that position. | |
| | `avg_elo` | INTEGER | The average rating of the two players. | |
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|
| --- |
|
|
| ## 🕰️ Dataset 2: Nexus-Core Legacy Data (`chess_stats_v2.db`) |
|
|
| This is the **original, large-scale dataset** that powered the **Nexus-Core** engine. It provides a broad foundation of solid, club-level chess knowledge. |
|
|
| ### Data Engineering & Filtering |
| * **Source:** Lichess Public Database (January 2017). |
| * **Critical Filter:** Only games where both players had an ELO **greater than 2000** were accepted. |
| * **Extraction:** Positions were extracted up to the first **20 moves** (Opening/Early Middlegame). |
| * **Final Volume:** Over 5,000,000 total positions processed, resulting in **2,488,753 unique positions**. |
| * **File Size:** **882 MB**. |
|
|
| ### Schema: `positions` table |
| | Column | Type | Description | |
| |--------|------|-------------| |
| | `fen` | TEXT (PK) | The board position, truncated to 4 parts (Position, Turn, Castling, En Passant). | |
| | `stats` | TEXT (JSON) | A JSON string containing aggregated move counts and game outcomes (Win/Draw/Loss). | |
|
|
| --- |
|
|
| ## 🚀 Usage Example (Python) |
|
|
| This example shows how to load and sample the **new Synapse-Base data**. |
|
|
| ```python |
| import sqlite3 |
| from huggingface_hub import hf_hub_download |
| |
| # Download the new Match Data |
| db_path = hf_hub_download( |
| repo_id="GambitFlow/Elite-Data", |
| filename="match_positions_v2.db", |
| repo_type="dataset" |
| ) |
| |
| # Connect and sample data |
| conn = sqlite3.connect(db_path) |
| cursor = conn.cursor() |
| |
| # Get 5 random middlegame positions |
| cursor.execute("SELECT fen, move_played, value_target FROM positions WHERE phase='midgame' ORDER BY RANDOM() LIMIT 5") |
| |
| for row in cursor.fetchall(): |
| print(f"FEN: {row}") |
| print(f"Grandmaster Move: {row} | Outcome Score: {row}") |
| print("-" * 30) |
| |
| conn.close() |
| ``` |
|
|
| --- |
| <div align-center |
