--- license: cc-by-nc-4.0 task_categories: - reinforcement-learning - image-classification tags: - chess - game-ai - deep-learning - synapse-edge - gambitflow language: - en pretty_name: Synapse-Edge Massive Chess Corpus size_categories: - 1M ![GambitFlow Banner](https://capsule-render.vercel.app/api?type=waving&color=0:2c3e50,100:000000&height=200§ion=header&text=Synapse-Edge%20Data&fontSize=50&animation=fadeIn&fontAlignY=35&desc=8.5M%20Fused%20Tactical%20&%20Positional%20Samples&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/) ![Size](https://img.shields.io/badge/Size-~1.12%20GB-blue) ![Samples](https://img.shields.io/badge/Samples-8,559,282-orange) ![Format](https://img.shields.io/badge/Format-SQLite%20(Sharded)-green) [**Associated Model**](https://huggingface.co/GambitFlow/Synapse-Edge) • [**Source: Lichess**](https://database.lichess.org/) ## 📖 Dataset Overview **Synapse-Edge-Data** is a high-performance, large-scale dataset specifically engineered for training Gen-3 chess engines like **Synapse-Edge v1**. It represents a revolutionary fusion of high-level human positional play and rigorous tactical patterns. Unlike previous datasets that focus solely on game outcomes, this corpus includes **3 million+ tactical puzzles**, enabling models to learn "the art of the kill" alongside "the art of strategy." --- ## 🏗️ Dataset Structure The dataset is delivered as a sharded SQLite system, optimized for **Distributed Training** across multiple machines or accounts. ### SQLite Schema (`training_data` table) | Column | Type | Description | | :--- | :--- | :--- | | `fen` | TEXT (PK) | The Forsyth-Edwards Notation of the board state (Position). | | `position_stats` | TEXT (JSON) | Detailed win/loss/draw statistics from master games (if available). | | `best_move` | TEXT | The calculated optimal UCI move for the position. | | `is_tactical` | INTEGER | Flag: `1` for tactical puzzles, `0` for positional master games. | | `difficulty` | TEXT | Skill classification: `beginner`, `intermediate`, or `advanced`. | ### Data Distribution (Shards) To facilitate training on hardware like Google Colab, the dataset is split into **4 shards**: - `shards/synapse_shard_1.db` (~1.38M positions) - `shards/synapse_shard_2.db` (~1.38M positions) - `shards/synapse_shard_3.db` (~1.38M positions) - `shards/synapse_shard_4.db` (~1.38M positions) --- ## 🛠️ Dataset Creation ### Curation Rationale Most open-source chess datasets are either purely positional (games) or purely tactical (puzzles). **Synapse-Edge-Data** bridges this gap. By combining 5.5M elite positions with 3M tactical patterns, we ensure that trained models don't just "play well" but can also find clinical tactical solutions under pressure. ### Source Data 1. **Elite Positional Data:** Extracted from Lichess rated games where both players had an **ELO > 2000**. 2. **Tactical Database:** Processed from the official Lichess Puzzle Database, filtering for themes like *Forks, Pins, Skewers, Discovered Attacks,* and *Mate-in-X*. ### Data Processing Pipeline 1. **Decompression:** Raw `.pgn.zst` and `.csv.zst` files were streamed and decoded. 2. **Cleaning:** Duplicate FENs were removed to prevent overfitting. 3. **Normalization:** FENs were truncated to 4 fields (removing half-move clock) for higher uniqueness. 4. **Best Move Calculation:** Positional data was enriched by selecting the move with the highest success rate in master games. --- ## 🚀 Use Cases ### Direct Use - **Training Chess Engines:** Specifically designed for Multi-Head Neural Networks (Policy/Value/Tactical heads). - **Game Analysis:** Can be used to train models that detect "blunders" or "sharp" positions. ### Out-of-Scope Use - Training for variants like *Chess960* or *Antichess* (this dataset is for **Standard Chess** only). - Non-chess board games. --- ## 🛡️ Bias, Risks, and Limitations - **ELO Bias:** Since the data is sourced from players > 2000 ELO, the model may struggle to exploit beginner-level "random" blunders effectively without further fine-tuning. - **Engine Bias:** Some "best moves" in the positional set reflect human theory, which might differ from pure engine evaluations at extreme depths. ## 📜 License This dataset is published under the **Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)** license. It is intended for research and educational purposes. Commercial use requires prior authorization. ---

Maintained by Rafsan @ GambitFlow Labs