datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
pawn-stockfish-100m
PAWN Stockfish 100M
100,000,000 self-play chess games generated with Stockfish 18, each
annotated with per-position, per-legal-move evaluations — for chess
policy-learning and NNUE-distillation research.
Dataset Summary
100,000,000 machine-generated self-play chess games. Every position in
every game is annotated with an evaluation of every legal move, not just
the move played. The dataset was built as training data for
PAWN — a testbed for finetuning
and… See the full description on the dataset page: https://huggingface.co/datasets/thomas-schweich/pawn-stockfish-100m.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:… See the full description on the dataset page: https://huggingface.co/datasets/mateuszgrzyb/lichess-stockfish-normalized.stockfish-19-soft-targets
avewright/stockfish-19-soft-targets
Official Stockfish 19 MultiPV soft targets, mined from the Lichess ECO
opening set. In-progress snapshot toward 1M unique positions.
600,000 rows in this upload. Source id 4. Vocab compact (1968).
How positions are chosen
Games start from the Lichess Chess Openings dataset
(lichess-org/chess-openings): 3,810 named
leaves (HF card still lists 3,704) plus
book prefixes, 7,852 unique starts.
ECO volumes: A 817 / B 772 /
C 1,250 / D… See the full description on the dataset page: https://huggingface.co/datasets/avewright/stockfish-19-soft-targets.chess-sft-10m-stockfishstockfish-policy
Stockfish Policy Teacher (100k)
Root-policy supervision for 100,000 chess positions, produced by a fixed-node
MultiPV Stockfish search. Each row carries a full probability distribution over
every legal move at the root — not just the best move — so it can be used as
a soft-label teacher for policy distillation.
What makes a row
For every legal root move the generator first evaluates the child position with
go depth 0, negating the child score into the root… See the full description on the dataset page: https://huggingface.co/datasets/Pawitt/stockfish-policy.chess-stockfish-analysisstockfish-selfplay