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Alfredvc
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chess-autocomplete-eval-datasets

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chess
lichess
evaluation
benchmark
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chess-autocomplete-eval-datasets / README.md
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Name the mixed license explicitly (CC0-1.0 Lichess-derived + MIT Allie/Maia-2)
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metadata 
license: other
license_name: mixed-cc0-1.0-and-mit
license_link: >-
  https://huggingface.co/datasets/Alfredvc/chess-autocomplete-eval-datasets/blob/main/README.md#licenses
pretty_name: Chess Autocomplete Eval Battery Datasets
tags:
  - chess
  - lichess
  - evaluation
  - benchmark
size_categories:
  - 1M<n<10M
configs:
  - config_name: maia_table1
    data_files:
      - split: test
        path: maia/example_test_dataset.csv
  - config_name: stratified_metadata
    data_files:
      - split: metadata
        path: stratified_human/merged-metadata.parquet

Chess Autocomplete Eval Battery Datasets

Input data for the full evaluation suite of the chess-autocomplete releases (91M, 350M, 700M). Inputs only: no model, eval code, or results. One directory per suite component.

README.md
index.json
LICENSES/
  allie-MIT.txt
  maia2-MIT.txt
allie_benchmark/
  2022-test-annotated.jsonl
maia/
  cross_skill_2023-12.jsonl
  example_test_dataset.csv
stratified_human/
  merged-map.bin
  merged-metadata.parquet
  merged.bin

Total size: 5.6 GiB (6.0 GB).

stratified_human/

  • Files: merged.bin, merged-map.bin, merged-metadata.parquet.
  • Contents: 768,000 Lichess standard games from 2022 and 2023, 45,728,786 plies. Stratified into 768 cells: 16 White-Elo buckets by 16 Black-Elo buckets by 3 time controls (bullet, blitz, rapid), 1,000 games per cell. Classical excluded.
  • Encoding: same binary token format as Alfredvc/chess-autocomplete-lichess. merged.bin holds 46,496,786 uint16 tokens, one per ply plus one game-end token per game; merged-map.bin holds uint64 per-game byte offsets (768,000 entries); merged-metadata.parquet holds one row per game.
  • Generation: the Rust chess-bin-stratify tool (rust/src/bin/chess_bin_stratify.rs) re-buckets per-division binary triplets that chess-pgn-stratify encoded from the monthly Lichess .pgn.zst archives.
  • Read by: the stratified_human component (chess_autocomplete/evals/components/stratified_human.py).
  • License: CC0-1.0. Derived from Lichess CC0 data.

allie_benchmark/

  • File: 2022-test-annotated.jsonl.
  • Contents: 884,049 scored positions from Lichess 2022 blitz games. Opening plies and moves played with under 30 seconds on the clock are excluded.
  • Source: file lichess-2022-blitz-test/2022-test-annotated.jsonl from the Hugging Face dataset yimingzhang/allie-data, fetched by scripts/download_allie_test_set.py.
  • Read by: the allie_benchmark component (chess_autocomplete/evals/components/allie/).
  • License: MIT (Allie, Zhang et al., 2024). Underlying games are Lichess CC0. See LICENSES/allie-MIT.txt.

maia/

example_test_dataset.csv

  • Contents: 127,852 rows of scored positions from Lichess December 2019 rapid games, sub-30-second moves already excluded. The maia component scores the 106,740 of them with move_ply > 10, dropping plies 0 through 10. Grouped Skilled (56,812), Advanced (41,747), Master (8,181).
  • Source: the Maia-2 project copy, Google Drive id 1fSu4Yp8uYj7xocbHAbjBP6DthsgiJy9X.
  • Read by: the maia component, surface table1.
  • License: MIT (Maia-2, CSSLab, 2024). Underlying games are Lichess CC0. See LICENSES/maia2-MIT.txt.

cross_skill_2023-12.jsonl

  • Contents: 4,932,999 scored positions from Lichess December 2023 rapid games. Per active-Elo by opponent-Elo cell, counts match the Maia-2 Appendix Table 8 matrix. Row schema tag chess_autocomplete.maia2_cross_skill_regenerated.v1.
  • Generation: generate_maia_cross_skill_rows in scripts/run_eval_suite.py scans lichess_db_standard_rated_2023-12.pgn.zst from database.lichess.org in source order and keeps rows per cell until each target count is met.
  • Read by: the maia component, surface cross_skill_figure2.
  • License: CC0-1.0. Derived from Lichess CC0 data.

Licenses

Path License Source Notice
stratified_human/* CC0-1.0 Lichess DB, re-encoded
maia/cross_skill_2023-12.jsonl CC0-1.0 Lichess DB, regenerated
allie_benchmark/2022-test-annotated.jsonl MIT Allie (Zhang et al., 2024) LICENSES/allie-MIT.txt
maia/example_test_dataset.csv MIT Maia-2 (CSSLab, 2024) LICENSES/maia2-MIT.txt

The Allie and Maia sets are redistributed under their MIT terms. The Lichess database is released under CC0.

Training exclusion

The released models are not trained on the Lichess months these sets cover: 2019-12, 2023-12, and 2022. Keeping those months out of training is why this repo exists. All three releases (91M, 350M, 700M) train on the same 21 monthly shards, 2024-01 through 2025-09, listed under data_config.shards in each run's training_config.yaml in chess-autocomplete-v1-checkpoints. Every evaluation month here predates that window.

Download 

hf download Alfredvc/chess-autocomplete-eval-datasets \
    --repo-type dataset --local-dir eval-datasets

The eval suite (scripts/run_eval_suite.py) fetches each file from this repo when it is not present locally.

File manifest

file size sha256
LICENSES/allie-MIT.txt 1.0 KiB de02e92381b5ccc5…
LICENSES/maia2-MIT.txt 1.0 KiB dad60c955a91b9d9…
allie_benchmark/2022-test-annotated.jsonl 42.7 MiB a6014de8ef861b2e…
maia/cross_skill_2023-12.jsonl 5.5 GiB 17eb3c88a61e3837…
maia/example_test_dataset.csv 21.5 MiB cd4defb7213f052e…
stratified_human/merged-map.bin 5.9 MiB b47598f23f8fd0a1…
stratified_human/merged-metadata.parquet 487.6 KiB 9ab335ccdcd59fc1…
stratified_human/merged.bin 88.7 MiB f0e6df96e931db4e…