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README.md

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-{}
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+license: cc-by-nc-4.0
+task_categories:
+- reinforcement-learning
+- tabular-classification
+language:
+- en
+tags:
+- chess
+- gambitflow
+- big-data
+- elite
+- sqlite
+size_categories:
+- 1M<n<10M
+pretty_name: GambitFlow Elite Training Data
+---
+
+# 📚 GambitFlow Elite Training Data
+
+<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)
+
+[![License: CC BY-NC 4.0](https://img.shields.io/badge/License-CC%20BY--NC%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by-nc/4.0/)
+![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)
+
+[**View on GitHub**](https://github.com/GambitFlow/GambitFlow) • [**Source Model: Nexus-core CE**](https://huggingface.co/GambitFlow/gambitflow-nexus-core)
+
+</div>
+
+## 📖 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.
+
+---
+<div align="center">
+  <p>Curated by <a href="https://github.com/GambitFlow">GambitFlow</a></p>
+</div>