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  ---
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- license: cc-by-nc-4.0
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  library_name: onnx
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  tags:
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  - chess
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  - game-ai
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  - basic model
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  datasets:
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- - Rafs-an09002/chessmate-opening-stats
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  language:
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  - en
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  pipeline_tag: reinforcement-learning
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  ---
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- # ♟️ ChessMate AI - CNN Evaluation Model
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  <div align="center">
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- ![ChessMate Banner](https://capsule-render.vercel.app/api?type=waving&color=0:3498db,100:2c3e50&height=180&section=header&text=ChessMate%20Model&fontSize=50&animation=fadeIn&fontAlignY=35&desc=ONNX%20Chess%20Evaluation%20Network)
 
 
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- [![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/)
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  ![Format](https://img.shields.io/badge/Format-ONNX-blue)
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  ![Input](https://img.shields.io/badge/Input-12x8x8%20Tensor-orange)
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- [**Live Demo**](https://chessmate-engine.onrender.com) • [**GitHub Repository**](https://github.com/Rafsan1711/Chessmate-Engine)
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  </div>
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  This is a **Convolutional Neural Network (CNN)** trained to evaluate chess positions. It takes a board state as input and outputs a scalar evaluation score between `-1` (Black winning) and `+1` (White winning).
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- It is the core "brain" of the **ChessMate AI** project, designed to run efficiently in web browsers using `onnxruntime-web`.
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  - **Architecture:** 3-Layer CNN with Batch Normalization and ReLU activation.
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  - **Framework:** Trained in PyTorch, exported to ONNX (Opset 14).
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  ## ⚠️ License & Limitations
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- This model is licensed under **CC BY-NC 4.0** (Attribution-NonCommercial 4.0 International).
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-
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- **You are free to:**
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- - Use this model for research, education, and personal projects.
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- - Modify and adapt the model.
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-
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- **You may NOT:**
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- - Sell this model or use it in a commercial product without permission.
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  ---
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+ license: gpl-3.0
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  library_name: onnx
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  tags:
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  - chess
 
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  - game-ai
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  - basic model
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  datasets:
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+ - GambitFlow/Starter-Data
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  language:
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  - en
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  pipeline_tag: reinforcement-learning
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  ---
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+ # ♟️ Nexus-Nano - CNN Evaluation Model
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  <div align="center">
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+ ![GabmbitFlow Banner](https://capsule-render.vercel.app/api?type=waving&color=0:3498db,100:2c3e50&height=180&section=header&text=Nexus%20Nano&fontSize=50&animation=fadeIn&fontAlignY=35&desc=ONNX%20Chess%20Evaluation%20Network)
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+
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+ [![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)
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  ![Format](https://img.shields.io/badge/Format-ONNX-blue)
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  ![Input](https://img.shields.io/badge/Input-12x8x8%20Tensor-orange)
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  </div>
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  This is a **Convolutional Neural Network (CNN)** trained to evaluate chess positions. It takes a board state as input and outputs a scalar evaluation score between `-1` (Black winning) and `+1` (White winning).
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+ It is the core "brain" of the **Nexus-Nano** project, designed to run efficiently in web browsers using `onnxruntime-web`.
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  - **Architecture:** 3-Layer CNN with Batch Normalization and ReLU activation.
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  - **Framework:** Trained in PyTorch, exported to ONNX (Opset 14).
 
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  ## ⚠️ License & Limitations
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+ This model is licensed under **GPL v3 (GNU General Public License Version 3)**
 
 
 
 
 
 
 
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  ---
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