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README.md

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+---
+license: mit
+task_categories:
+  - tabular-classification
+  - other
+tags:
+  - chess
+  - game-ai
+  - evaluation
+size_categories:
+  - 1B<n<10B
+---
+
+# 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](https://huggingface.co/datasets/Lichess/chessbenchmate) dataset.
+
+## License
+
+MIT License (same as source dataset)

dataset.py

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+#!/usr/bin/env python3
+"""Dataset loader for ChessBenchmate Aggregated dataset."""
+
+import msgpack
+from pathlib import Path
+from typing import Iterator, Dict, Any
+
+
+def stream_positions(data_dir: str, shard_range: tuple = None) -> Iterator[Dict[str, Any]]:
+    """Stream positions from all shards.
+
+    Args:
+        data_dir: Directory containing the .msgpack files
+        shard_range: Optional (start, end) tuple to limit shards
+
+    Yields:
+        Dict with 'fen' and 'moves' keys
+    """
+    data_path = Path(data_dir)
+    shard_files = sorted(data_path.glob('train-*.msgpack'))
+
+    if shard_range:
+        start, end = shard_range
+        shard_files = shard_files[start:end]
+
+    for shard_file in shard_files:
+        with open(shard_file, 'rb') as f:
+            unpacker = msgpack.Unpacker(f, raw=False)
+            for record in unpacker:
+                yield record
+
+
+def count_positions(data_dir: str) -> tuple:
+    """Count total positions and moves in the dataset.
+
+    Returns:
+        Tuple of (num_positions, num_moves)
+    """
+    total_positions = 0
+    total_moves = 0
+
+    for record in stream_positions(data_dir):
+        total_positions += 1
+        total_moves += len(record['moves'])
+
+    return total_positions, total_moves
+
+
+if __name__ == '__main__':
+    import sys
+
+    if len(sys.argv) < 2:
+        print("Usage: python dataset.py <data_dir>")
+        sys.exit(1)
+
+    data_dir = sys.argv[1]
+
+    print(f"Sampling positions from {data_dir}...")
+    for i, record in enumerate(stream_positions(data_dir)):
+        print(f"\nPosition {i+1}:")
+        print(f"  FEN: {record['fen']}")
+        print(f"  Moves: {len(record['moves'])}")
+        for move, eval in list(record['moves'].items())[:3]:
+            print(f"    {move}: win_prob={eval['win_prob']:.3f}, mate={eval['mate']}")
+        if i >= 4:
+            break

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