README.md

---
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 # Updated to reflect total size
pretty_name: GambitFlow Elite Training Data (Unified)
---

# 📚 GambitFlow Elite Training Data (Unified)

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![Dataset Banner](https://capsule-render.vercel.app/api?type=waving&color=0:27ae60,100:2c3e50&height=200&section=header&text=Elite%20Training%20Data&fontSize=50&animation=fadeIn&fontAlignY=35&desc=Unified%20Master%20Collections%20(Legacy%20Core%20+%20Modern%20Synapse)&descAlignY=60)
[![License: CC0-1.0](https://img.shields.io/badge/License-CC0%201.0-lightgrey.svg)](http://creativecommons.org/publicdomain/zero/1.0/) 
![Format](https://img.shields.io/badge/Format-SQLite3-green)
![Total Size](https://img.shields.io/badge/Total%20Size-~2.1GB-blue)

[**View on GitHub**](https://github.com/GambitFlow/GambitFlow) • [**Target Models: Nexus-Core & Synapse-Base**](https://huggingface.co/GambitFlow)

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## 📖 Dataset Overview

This repository hosts the **foundational knowledge bases** for the GambitFlow chess engines. It consolidates two distinct, powerful datasets:

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.

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`)

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. |

---

## 🕰️ 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()
```

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