configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- config_name: full
data_files:
- split: train
path: full/train-*
tags:
- chess
pretty_name: Lichess Elite Database in UCI format
dataset_info:
config_name: full
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 11624616189
num_examples: 27272283
download_size: 6905406421
dataset_size: 11624616189
Lichess.org Elite Database in UCI format
This dataset was created using the lichess elite database.
It includes the all games up to December 2024.
The full list of files included in this dataset are located in lichess_file_list.txt
.
After downloading the zip files, the zip files were processed using the following script. The goal was to create three UCI-encoded datasets from the lichess elite datset, where all games are deduplicated across all three datasets:
- has_promote: includes non-checkmate games that include pawn promotions
- no_promote: includes non-checkmate games without pawn promotions
- checkmates: includes games that end in checkmate
#!/usr/bin/env fish
# Deduplicate all Lichess elite database files
pgn-extract --notags --nocomments --nonags --novars -w10000 --noduplicates -o all_deduplicated.san -f lichess_file_list.txt 2>dedupe_output.txt
echo "Deduplication complete. Output saved to all_deduplicated.san."
# Partition games into checkmates and non-checkmates
pgn-extract --notags --nocomments --nonags --novars -w100000 --checkmate -o checkmates.san -n others.san all_deduplicated.san 2>/dev/null
echo "Games partitioned: checkmates.san and others.san created."
# Further partition non-checkmate games based on pawn promotions
grep -B1 "=" others.san > has_promote.san
grep -B1 -v "=" others.san > no_promote.san
echo "Non-checkmate games split into has_promote.san and no_promote.san."
# Convert each SAN file to UCI format
pgn-extract -Wlalg --noresults --nochecks --nomovenumbers --notags --nocomments --nonags --novars -w100000 -o has_promote.uci has_promote.san 2>/dev/null
pgn-extract -Wlalg --noresults --nochecks --nomovenumbers --notags --nocomments --nonags --novars -w100000 -o no_promote.uci no_promote.san 2>/dev/null
pgn-extract -Wlalg --noresults --nochecks --nomovenumbers --notags --nocomments --nonags --novars -w100000 -o checkmates.uci checkmates.san 2>/dev/null
echo "SAN files converted to UCI format."
# Add "#" to the end of each move sequence in checkmates.uc
sed -i '/./s/$/#/' checkmates.uci
echo "Checkmate sequences updated with '#' as EOS token."
# Remove all blank lines
sed -i '/^$/d' has_promote.uci
sed -i '/^$/d' no_promote.uci
sed -i '/^$/d' checkmates.uci
echo "Blank lines removed. Finished."
Once the raw files were processed, the UCI-encoded files contained the following number of games:
- checkmates.uci: 3,708,644 games that end in checkmate
- no_promote.uci: 23,201,987 games that do not end in checkmate and have no pawn promotions
- has_promote.uci: 361,652 games that do not end in checkmate and have at least one pawn promotion
I wanted to balance the number of games from each category with a bias toward games that end in checkmate (so the LLM learns to finish the game rather than simply carry-on playing without a goal).
To accomplish this, I selected all games from checkmates.uci
and has_promote.uci
, and selected games from the no_promote.uci
at a 5:1 ratio to the number of games in has_promote.uci
, i.e., 1,808,260 games.
A simple python script for this is:
from datasets import load_dataset, concatenate_datasets
checkmates = load_dataset("text", data_files="lichess-elite/checkmates.uci")["train"]
no_promote = load_dataset("text", data_files="lichess-elite/no_promote.uci")["train"]
has_promote = load_dataset("text", data_files="lichess-elite/has_promote.uci")["train"]
# shuffle to ensure we're selecting across the entire dataset
no_promote_subset = no_promote.shuffle(seed=42).select(range(5 * len(has_promote)))
# shuffle entire dataset
ds = concatenate_datasets([checkmates, has_promote, no_promote_subset]).shuffle(seed=42)
Special thanks
Special thanks to nikonoel and the curators of the Lichess Elite Database.
Citation Information
If you use this dataset, please cite it as follows:
@misc{lichess_uci,
author = {Davis, Austin},
title = {Lichess.org Elite Database in UCI format},
year = {2025},
howpublished = {\url{https://huggingface.co/datasets/austindavis/lichess-elite-uci}},
}