Update README.md

README.md

@@ -8,63 +8,111 @@ language:
 tags:
 - chess
 - gambitflow
-- big-data
+- synapse-base
+- nexus-core
 - elite
 - sqlite
+- big-data
 size_categories:
-- 1M<n<10M
-pretty_name: GambitFlow Elite Training Data
+- 10M<n<100M # Updated to reflect total size
+pretty_name: GambitFlow Elite Training Data (Unified)
 ---
 
-# 📚 GambitFlow Elite Training Data
+# 📚 GambitFlow Elite Training Data (Unified)
 
 <div align="center">
 
-![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=5%20Million%2B%20Master%20Level%20Positions%20(ELO%202000%2B)&descAlignY=60)
+![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)
-![Volume](https://img.shields.io/badge/Size-882MB-blue)
-![Quality](https://img.shields.io/badge/Quality-Filtered%202000%2B%20ELO-red)
+![Total Size](https://img.shields.io/badge/Total%20Size-~1GB-blue)
 
-[**View on GitHub**](https://github.com/GambitFlow/GambitFlow) • [**Source Model: Nexus-core CE**](https://huggingface.co/GambitFlow/gambitflow-nexus-core)
+[**View on GitHub**](https://github.com/GambitFlow/GambitFlow) • [**Target Models: Nexus-Core & Synapse-Base**](https://huggingface.co/GambitFlow)
 
 </div>
 
-## 📖 Dataset Description
+## 📖 Dataset Overview
+
+This repository hosts the **foundational knowledge bases** for the GambitFlow chess engines. It consolidates two distinct, powerful datasets:
 
-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.
+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.
 
-By exclusively training on **Elite-level games**, the resulting AI avoids learning common amateur mistakes and focuses on solid positional principles.
+Together, they provide a comprehensive training resource covering different eras of chess theory and rating levels.
+
+---
 
-## 🛠️ Data Engineering & Filtering
+## 💎 Dataset 1: Synapse-Base Match Data (`match_positions_v2.db`)
 
-The database was created through a multi-stage, streaming pipeline to handle the massive volume efficiently without memory overflow.
+This is the **newly added**, highly-focused dataset designed to teach **Synapse-Base** advanced middlegame strategy and endgame technique. It prioritizes quality over quantity.
 
-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.
+### 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 **3,000,000** strategically rich positions.
 
-- **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 | 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. |
+
+---
 
-## 📂 File Structure & Schema
+## 🕰️ Dataset 2: Nexus-Core Legacy Data (`chess_stats_v2.db`)
 
-The main file is `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.
 
-### Table: `positions`
+### 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 (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. |
+| `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 (Model Training)
+---
+
+## 🚀 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
 
-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.
+# 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">
-  <p>Curated by <a href="https://github.com/GambitFlow">GambitFlow</a></p>
-</div>
+<div align-center