Add dataset card (provenance, stats, format, load instructions)

README.md

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+---
+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)