README.md

metadata

license: mit
pretty_name: Zero Evaluator High-Variance Chess Positions
language:
  - en
tags:
  - chess
  - leela-chess-zero
  - stockfish
  - chess-game
  - parquet
  - fen
size_categories:
  - 1M<n<10M
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/source_split=train/**/*.parquet
      - split: test
        path: data/source_split=test/**/*.parquet
  - config_name: stockfish_zero_wdl
    data_files:
      - split: train
        path: stockfish-zero-wdl/source_split=train/**/*.parquet
      - split: test
        path: stockfish-zero-wdl/source_split=test/**/*.parquet

Zero Evaluator High-Variance Chess Positions

This dataset contains 1,509,201 unique chess positions extracted from Leela Chess Zero's published CCRL standard corpus. Positions are stored as normalized six-field FEN records for immediate board reconstruction without replaying a game.

The selection deliberately balances opening, middlegame, and endgame coverage and retains the original source train/test split. The complete variance audit is in variance-report.json and RESULTS.md.

Two configurations are available:

• default: the original unlabeled positions;
• stockfish_zero_wdl: the same positions with immediate static Stockfish NNUE WDL labels and per-row engine provenance.

Data layout

The release consists of six Zstandard-compressed Parquet files partitioned by:

• source_split: train or test
• phase: opening, middlegame, or endgame

Split	Opening	Middlegame	Endgame	Total
Train	182,691	723,841	295,196	1,201,728
Test	51,510	176,159	79,804	307,473
Total	234,201	900,000	375,000	1,509,201

data/_manifest.json contains file sizes, row counts, ID bounds, and SHA-256 checksums.

The labeled derivative mirrors the same six partitions under stockfish-zero-wdl/. Its _manifest.json records the source Hub revision, engine identity, binary checksum, row counts, file sizes, and checksums.

Load the dataset

Hugging Face Datasets:

from datasets import load_dataset

positions = load_dataset("Pawitt/zero-evaluator")
print(positions["train"][0]["fen"])

Load the Stockfish-labeled configuration with:

from datasets import load_dataset

positions = load_dataset(
    "Pawitt/zero-evaluator",
    "stockfish_zero_wdl",
)
row = positions["train"][0]
print(row["fen"], row["wdl_win"], row["wdl_draw"], row["wdl_loss"])

For Hive partition columns and streaming Arrow batches, use PyArrow directly:

import pyarrow.dataset as ds

positions = ds.dataset(
    "data",
    format="parquet",
    partitioning="hive",
)
scanner = positions.scanner(
    filter=ds.field("source_split") == "train",
    columns=["fen", "result", "phase"],
    batch_size=8192,
)
for batch in scanner.to_batches():
    pass

When downloading from the Hub first, point ds.dataset at the downloaded data/ directory.

Columns

Each record includes normalized fen, source-game provenance, ply and result, side to move, piece and material statistics, legal-move count, check state, castling mask, and halfmove clock. See FORMAT.md for exact semantics.

The source-game result is provenance metadata. It is not an lc0 depth-zero WDL label.

The stockfish_zero_wdl configuration adds:

Column	Type	Meaning
wdl_win	uint16	Static win probability on a 0–1000 scale
wdl_draw	uint16	Static draw probability on a 0–1000 scale
wdl_loss	uint16	Static loss probability on a 0–1000 scale
wdl_engine_name	string	Evaluating engine family
wdl_engine_version	string	Exact engine build identity
wdl_engine_weights	string	NNUE network identity

WDL is from the perspective of the side to move and always sums to 1000.

Stockfish zero-depth labeling

The labeled configuration was produced with a patched Stockfish command, go depth 0. It performs one immediate NNUE evaluation without tree search, then applies Stockfish's calibrated WDL conversion. This is a static evaluator label, not a searched game-theoretic result or a native three-output neural head.

Provenance:

• engine: Stockfish dev-20260822-d95a3013-zero-wdl;
• NNUE: nn-1a298aa575a0.nnue;
• engine binary SHA-256: 57ca9adcf657338ac907b3c0c667f1f705885c06865603134f23c6e6e402682d;
• source dataset revision: 7e7d453311882b4b8686aeb885e1c2eb9e2911f9;
• terminal adjudication: python-chess outcome(claim_draw=True).

Across all rows, the mean WDL is 186.773 / 578.242 / 234.985. Stockfish's static calibration is draw-heavy: the median draw value is 875/1000.

Validation

• All 1,509,201 source rows were reproduced in Parquet.
• All six partition counts match the source database.
• All 26 row groups use Zstandard compression.
• All six file checksums match the manifest.
• 6,000 sampled FEN records were reconstructed successfully with python-chess.
• The Stockfish derivative contains exactly 1,509,201 rows in the same six partitions.
• Every labeled partition matches its manifest row count, byte size, and SHA-256 checksum.
• Every labeled row has WDL values summing to 1000.
• All labeled files carry zero_wdl_complete=true and consistent engine provenance.

Source

The source is the Leela Chess Zero standard CCRL dataset, published as 2.5 million CCRL 40/40 and 40/4 engine games with an original 80/20 train/test split.

The extraction and conversion scripts are included for reproducibility.