mateuszgrzyb/lichess-stockfish-normalized

Lichess Chess Positions: ML-Ready Deduplicated Evaluations

Dataset Description

A curated dataset of 316,072,343 unique chess positions with Stockfish evaluations, optimized for training neural networks. This is a deduplicated, ML-ready version of the Lichess evaluation database.

Why This Dataset?

While Lichess provides deduplicated evaluations in JSONL.zst format, and HuggingFace hosts the full (non-deduplicated) version, this dataset offers:

Unique advantages:

• ✅ Deduplicated (like Lichess source)
• ✅ Parquet format (5-10x faster loading than JSONL.zst)
• ✅ Split into 10 manageable parts (easy incremental downloads)
• ✅ Optimized for ML (removed unnecessary columns)
• ✅ 80% smaller than non-deduplicated version

Comparison:

Source	Duplicates	Format	Size	Splits
Lichess DB	None	JSONL.zst	~17GB (~83GB decompressed)	1 file
HF Lichess	Yes (784M rows)	Parquet	30GB+	16 parts
This dataset	None (316M rows)	Parquet	~7GB	10 parts

Perfect for researchers who want deduplicated data without decompressing 80GB+ JSONL.zst files.

Dataset Structure

Data Instance

One row of the dataset looks like this:

{
  "fen": "2bq1rk1/pr3ppn/1p2p3/7P/2pP1B1P/2P5/PPQ2PB1/R3R1K1 w - -",
  "depth": 36,
  "cp": 311,
  "mate": null
}

Data Fields

Field	Type	Description
fen	string	Chess position in FEN notation (pieces, active color, castling rights, en passant)
depth	int	Search depth reached by Stockfish engine
cp	int	Centipawn evaluation (-∞ to +∞). null if mate is certain
mate	int	Moves until mate. null if mate is not certain

Data Splits

The dataset is split into 10 equally-sized parts (~32M positions each) for convenient downloading:

from datasets import load_dataset

# Load full dataset (all 10 parts)
dataset = load_dataset("mateuszgrzyb/lichess-stockfish-normalized", split="train")

# Or load specific percentage (faster download)
dataset = load_dataset("mateuszgrzyb/lichess-stockfish-normalized", split="train[:10%]")

# Or load by number of examples
dataset = load_dataset("mateuszgrzyb/lichess-stockfish-normalized", split="train[:1000000]")

Dataset Creation

Source Data

Original data: Lichess evaluation database

• Source: Lichess analysis board
• Evaluator: Stockfish (various versions and depths)
• Collection: Produced by Lichess users running Stockfish in browser during analysis
• Update frequency: Monthly (last updated: November 2025)

Preprocessing Pipeline

The preprocessing was performed as part of the Searchless Chess project:

1. Data Loading: Loaded in parts de-normalized posiotions with evaluations Lichess/chess-position-evaluations (~37GB)
2. Deduplication: For each unique FEN, retained only the evaluation with maximum depth
   • Original: 784M rows with duplicates
   • After dedup: 316M unique positions
3. Column removal: Removed line and knodes fields (not needed for position evaluation)
4. Format conversion: JSONL (original file in Lichess DataBase) → Parquet (faster I/O for ML workflows)
5. Partitioning: Split into 10 equal parts for manageable downloads

Size reduction: ~83GB (decompressed JSONL) | ~37GB (Parquet) → ~7GB (deduplicated Parquet) = over 80% reduction

Quality Metrics

• Unique positions: 316,072,343
• Average file size: ~650MB per part

Usage Example

Basic Loading

from datasets import load_dataset

# Load full dataset
dataset = load_dataset("mateuszgrzyb/lichess-stockfish-normalized", split="train")

# Access data
print(f"Total: {len(dataset)}")
print(dataset[0])

Incremental Loading (Memory-Efficient)

from datasets import load_dataset

# Load one part at a time
for i in range(10):
    part = load_dataset(
        "mateuszgrzyb/lichess-stockfish-normalized",
        split=f"train[{i*10}%:{(i+1)*10}%]"
    )
    # Process part...
    train_on_part(part)

Citation

If you use this dataset in your research, please cite:

@dataset{grzyb2025lichess,
  author = {Grzyb, Mateusz},
  title = {Lichess Chess Positions: ML-Ready Deduplicated Evaluations},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/mateuszgrzyb/lichess-stockfish-normalized}}
}

And cite the original Lichess database:

@misc{lichess2024database,
  author = {Lichess},
  title = {Lichess Elite Database},
  year = {2024},
  url = {https://database.lichess.org}
}

Related Resources

• 📂 Project Repository: Searchless Chess on GitHub
• 📄 Inspiration: Grandmaster-Level Chess Without Search (DeepMind, 2024)
• ♟️ Original Dataset: Lichess Evaluation Database
• 🤗 Non-Deduplicated Version: Lichess/chess-position-evaluations

License

This dataset is licensed under CC BY 4.0.

Original data from Lichess is licensed under CC0 1.0 (Public Domain).

Dataset Curator

Created by Mateusz Grzyb as part of the Searchless Chess project.

Changelog

v1.0.0 (November 2025)

• Initial release
• 316M deduplicated positions
• 10-part split in Parquet format

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Dataset last updated: November 2025