Datasets:

GambitFlow
/

Synapse-Edge-Data

---
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<n<10M
---

# ♟️ GambitFlow Synapse-Edge-Data (Massive Corpus)

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![GambitFlow Banner](https://capsule-render.vercel.app/api?type=waving&color=0:2c3e50,100:000000&height=200&section=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/)

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## 📖 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."

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## 🏗️ 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)

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## 🛠️ 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.

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## 🚀 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.

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## 🛡️ 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.

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<div align="center">
  <p>Maintained by <a href="https://huggingface.co/Rafs-an09002">Rafsan</a> @ GambitFlow Labs</p>
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