metadata

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}},
}