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---
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](https://database.nikonoel.fr/).
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:
1. has_promote: includes non-checkmate games that include pawn promotions
1. no_promote: includes non-checkmate games without pawn promotions
1. checkmates: includes games that end in checkmate
```sh
#!/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:
```python
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](https://database.nikonoel.fr) 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}},
}
``` |