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prdev/chessbench-full-policy-value

ChessBenchmate Aggregated Dataset This dataset is a transformed version of the ChessBenchmate dataset, aggregating all legal moves and their Stockfish evaluations per chess position. Dataset Structure Each record contains: fen: Chess position in FEN notation moves: Dictionary mapping UCI moves to their evaluations win_prob: Win probability from 0.0 to 1.0 (Stockfish evaluation) mate: Mate indicator (None = no forced mate, '#' = immediate checkmate, integer =… See the full description on the dataset page: https://huggingface.co/datasets/prdev/chessbench-full-policy-value.

sourceHugging Facemitupdated 8mo agoView on Hugging Face
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ChessBenchmate Aggregated Dataset

This dataset is a transformed version of the ChessBenchmate dataset, aggregating all legal moves and their Stockfish evaluations per chess position.

Dataset Structure

Each record contains:

  • fen: Chess position in FEN notation
  • moves: Dictionary mapping UCI moves to their evaluations
  • win_prob: Win probability from 0.0 to 1.0 (Stockfish evaluation)
  • mate: Mate indicator (None = no forced mate, '#' = immediate checkmate, integer = mate-in-N)

File Format

  • Format: MessagePack binary (streamed records)
  • Files: 1024 shards (train-XXXXX-of-01024.msgpack)
  • Estimated: ~3.6B unique positions

Usage

python
import msgpack

def load_positions(filepath):
    """Stream positions from a msgpack file."""
    with open(filepath, 'rb') as f:
        unpacker = msgpack.Unpacker(f, raw=False)
        for record in unpacker:
            yield record

# Example
for record in load_positions('train-00000-of-01024.msgpack'):
    fen = record['fen']
    moves = record['moves']
    for move, eval in moves.items():
        print(f"{move}: win_prob={eval['win_prob']:.3f}, mate={eval['mate']}")
    break

Source

Transformed from ChessBenchmate dataset.

License

MIT License (same as source dataset)