| --- |
| license: cc0-1.0 |
| language: en |
| pretty_name: ChessModel-XPU formal teacher dataset |
| tags: |
| - chess |
| - alphazero |
| - stockfish |
| - knowledge-distillation |
| - game |
| - reinforcement-learning |
| size_categories: |
| - 1M<n<10M |
| --- |
| |
| # ChessModel-XPU formal teacher dataset |
|
|
| Stockfish-18-labeled chess positions used to train the |
| [`jinshuoli/chessmodel`](https://huggingface.co/jinshuoli/chessmodel) checkpoint, |
| part of the [ChessModel-XPU](https://github.com/JinShuo-Li/ChessModel) project. |
|
|
| It is a **custom-format** dataset of bit-packed board tensors plus sparse |
| Stockfish-derived policy / WDL targets. It is **not** loadable with |
| `datasets.load_dataset(...)`; use the project's `TeacherDataset` loader (see |
| below). Game-level train/validation split — adjacent positions from the same game |
| are never spread across splits. |
|
|
| ## Source and provenance |
|
|
| | | | |
| |---|---| |
| | Source games | [Lichess Elite database, December 2023](https://database.lichess.org/) (`lichess_elite_2023-12`), licensed **CC0 1.0** | |
| | Source SHA-256 | `a6a5a8253cf357d31b7b5c1895a63dfbf64cc93b4504398f748cb69a31c0eff0` | |
| | Teacher | Stockfish 18 — MultiPV 8, 10000 nodes/position, WDL enabled, temperature 0.15 | |
| | Split | Deterministic game-level 90 / 10 split, seed 7, minimum 16 plies per game | |
| | Prep script | [`scripts/prepare_formal_pgn.py`](https://github.com/JinShuo-Li/ChessModel/blob/main/scripts/prepare_formal_pgn.py) | |
|
|
| Preparation removed parse failures, non-standard start positions, games without a |
| decisive/draw result, short games (< 16 plies), and duplicate move sequences, then |
| made the deterministic game-level split. |
|
|
| ## Contents |
|
|
| The dataset mirrors the repository layout, so downloading it into a clone of the |
| project makes the workflow commands resolve unchanged: |
|
|
| | Path | Description | |
| |---|---| |
| | `data/formal_1m_train/shard-00000…00244.npz` | 245 training shards | |
| | `data/formal_50k_validation/shard-*.npz` | 13 validation shards | |
| | `datasets/formal_train.pgn` | Source training PGN (game-level split) | |
| | `datasets/formal_validation.pgn` | Source validation PGN (game-level split) | |
| | `datasets/formal_pgn_metadata.json` | Provenance metadata (source hash, counts, split params) | |
|
|
| Total ≈ 370 MB. |
|
|
| ## Statistics |
|
|
| From `datasets/formal_pgn_metadata.json`: |
|
|
| | | | |
| |---|---| |
| | Parsed games | 315,135 | |
| | Accepted games | 312,603 | |
| | Train / validation games | 281,003 / 31,600 | |
| | Train / validation positions | 2,971,862 / 334,836 | |
| | Validation percent | 10 | |
| | Seed | 7 | |
|
|
| ## Shard format |
|
|
| Each `.npz` shard is a project-defined record containing: |
|
|
| - Bit-packed board planes (`112 × 8 × 8`, canonically oriented to the side to move: |
| up to 8 history frames, castling rights, en-passant, side to move, halfmove and |
| fullmove clocks). |
| - Sparse policy target over the AlphaZero `8×8×73 = 4672` move encoding. |
| - Win/Draw/Loss target derived from Stockfish WDL. |
| - A shard format version, per-shard SHA-256 integrity check, Stockfish/node |
| metadata, and the FEN (for legal-move masking and audit). |
|
|
| `TeacherDataset` loads each shard's bit-packed boards and sparse targets once and |
| unpacks lazily, so the full million-position dataset stays out of resident memory. |
|
|
| ## How to load |
|
|
| Clone the repo and download the dataset into it, then use the project loader: |
|
|
| ```bash |
| git clone https://github.com/JinShuo-Li/ChessModel.git |
| cd ChessModel |
| hf download jinshuoli/chessmodel-data --repo-type dataset --local-dir . |
| ``` |
|
|
| ```python |
| from torch.utils.data import DataLoader |
| from chess_ai.data.dataset import TeacherDataset |
| |
| train = TeacherDataset("data/formal_1m_train") # 245 shards |
| val = TeacherDataset("data/formal_50k_validation") # 13 shards |
| loader = DataLoader(train, batch_size=512, shuffle=True) |
| ``` |
|
|
| Verify integrity with the project's verifier: |
|
|
| ```bash |
| python scripts/verify_teacher_dataset.py --dataset data/formal_1m_train |
| python scripts/verify_teacher_dataset.py --dataset data/formal_50k_validation |
| ``` |
|
|
| ## Intended use and limitations |
|
|
| - **Intended:** training / evaluating compact neural chess models via Stockfish |
| distillation, and reproducing the project's training pipeline. |
| - **Custom format:** not consumable by the HF dataset viewer or |
| `datasets.load_dataset()`; requires the project's loader. |
| - **Labels are Stockfish outputs:** policy/WDL targets reflect Stockfish 18 search |
| at the configured node budget, not human game outcomes (game results are used |
| only for splitting/filtering). |
|
|
| ## License and attribution |
|
|
| - Source game data: © Lichess, **CC0 1.0** (public domain). |
| - The loading code and preparation scripts are MIT-licensed in the |
| [source repository](https://github.com/JinShuo-Li/ChessModel). |
| - Stockfish is used solely as a teacher/labeling tool and is **not** distributed |
| here. |
|
|
| ## Related |
|
|
| - Trained checkpoint: [`jinshuoli/chessmodel`](https://huggingface.co/jinshuoli/chessmodel) |
| - Source code & documentation: [github.com/JinShuo-Li/ChessModel](https://github.com/JinShuo-Li/ChessModel) |
|
|
