library_name: transformers
tags:
- chess
- fen
- uci
datasets:
- bonna46/Chess-FEN-and-NL-Format-30K-Dataset
- Vasanth/chessdevilai_fen_dataset
base_model:
- openai-community/gpt2
Model Card for chess_model4
Model Description
The model was trained to be used for a chess-playing agent built on a fine-tuned GPT-2 model. It was trained for the player to take a board position in FEN format and returns a legal move in UCI notation.
- Developed by: Aliyah Vos
- Model type: Decoder Causal LM
- Finetuned from model: openai-community/gpt2
Model Sources
- Repository: almvos/Midtrm/Chess/Tournament
Uses
Direct Use
Given a chess board in FEN notation, the model predicts the next best move in the form of a UCI string.
Out-of-Scope Use
This model has been fine-tuned for chess move prediction.
Training Details
Training Data
A combination of different datasets was used to train the model
HF: "Vasanth/chessdevilai_fen_dataset"
HF: "bonna46/Chess-FEN-and-NL-Format-30K-Dataset"
Kaggle: "yousefradwanlmao/stockfish-best-moves-compilation"
Preprocessing
The different datasets were normalised to be in the same format and shuffled to combine. The kaggle dataset was filtered for missing "Best move" values.
Training Hyperparameters
learning_rate = 3e-5
metric_for_best_model = "eval_loss"
weight_decay = 0.01
warmup_ratio = 0.05