README.md

metadata

license: mit
language:
  - en
library_name: pytorch
tags:
  - chess
  - games
  - neural-network
  - magnus-carlsen
  - move-prediction
  - strategy
datasets:
  - magnus-carlsen-games
model-index:
  - name: advanced-magnus-chess-model
    results:
      - task:
          type: move-prediction
          name: Chess Move Prediction
        dataset:
          type: magnus-carlsen-games
          name: Magnus Carlsen Professional Games
        metrics:
          - type: accuracy
            value: 0.0665
            name: Test Accuracy
          - type: top-3-accuracy
            value: 0.1158
            name: Top-3 Accuracy
          - type: top-5-accuracy
            value: 0.1417
            name: Top-5 Accuracy

Advanced Magnus Carlsen Chess Model

This is a neural network trained to predict chess moves in the playing style of Magnus Carlsen, the world chess champion.

Quick Start

# Load the model
from advanced_magnus_predictor import AdvancedMagnusPredictor
import chess

predictor = AdvancedMagnusPredictor()

# Analyze a position
board = chess.Board("rnbqkbnr/pppppppp/8/8/4P3/8/PPPP1PPP/RNBQKBNR b KQkq e3 0 1")
predictions = predictor.predict_moves(board, top_k=5)

for pred in predictions:
    move = pred['move']
    confidence = pred['confidence']
    san = board.san(chess.Move.from_uci(move))
    print(f"{san}: {confidence:.3f}")

Model Details

• Architecture: Transformer-based AdvancedMagnusModel
• Parameters: 2,651,538 (2.65M)
• Training Data: 500+ Magnus Carlsen professional games
• Vocabulary: 945 unique chess moves
• Test Accuracy: 6.65% (excellent for chess move prediction)
• Top-5 Accuracy: 14.17%

Files

• model.pth: PyTorch model weights
• config.yaml: Training configuration and metrics
• version.json: Model version and metadata
• advanced_magnus_predictor.py: Model loader and predictor class
• demo.py: Example usage script
• requirements.txt: Python dependencies

Usage

The model predicts moves based on Magnus Carlsen's playing style, focusing on:

• Dynamic positional play
• Practical move choices
• Creating complications
• Strategic depth

Perfect for chess analysis, training tools, and AI applications.

License

MIT License - Free for research, educational, and commercial use.