---
language: en
license: mit
tags:
  - tic-tac-toe
  - xo
  - synthetic
  - board-game
  - classification
task_categories:
  - text-classification
size_categories:
  - 10K<n<100K
pretty_name: "FemtoXO Dataset (Synthetic XO Games)"
configs:
  - config_name: default
    data_files: data.jsonl
---

# FemtoXO Dataset – Synthetic Tic‑Tac‑Toe Games

**FemtoXO Dataset** is a large collection of board states and moves from randomly played Tic‑Tac‑Toe games.  
It was created to train the [FemtoXO model](https://huggingface.co/abdelkader-dev/FemtoXO), a tiny Transformer that plays as **X**.

## Dataset Summary

Each row represents one board position **where it is X’s turn to play**, along with the move X made in that turn.  
The dataset is entirely synthetic and was generated programmatically by simulating 10,000 full random games and recording every X‑turn state.

- **Total samples:** ≈ 90,000 (varies slightly due to different game lengths)
- **Format:** JSON Lines (`.json`)
- **Language:** not applicable (board symbols)

## Data Structure

Each line is a JSON object with two fields:

| Field   | Type    | Description |
|---------|---------|-------------|
| `board` | string (length 9) | Board state: `.` = empty, `X` = player X, `O` = player O |
| `move`  | integer (0–8) | The index of the cell (0‑based, row‑major) chosen by X |

**Example:**
```json
{"board": "X..O.....", "move": 4}
```

## Data Splits

The data is provided as a single file (`data.json`) containing all samples.  
During training we typically split it into:
- **Train:** 90%
- **Validation:** 10%

## Generation Process

1. Start with an empty 3×3 board (all `.`).
2. Players alternate turns (`X` first, then `O`), each choosing a random legal move.
3. Before every X move, save the current board state and the chosen move.
4. Game ends on a win or a draw (board full).

The complete generator script is available in the [FemtoXO repository](https://huggingface.co/abdelkader-dev/FemtoXO) under `src/train.py`.

## Usage

You can load the dataset directly with 🤗 Datasets:

```python
from datasets import load_dataset

dataset = load_dataset("abdelkader-dev/XO_matches")
print(dataset['train'][0])
```

## Known Limitations

- **Random strategy:** Moves are chosen uniformly, so the dataset does **not** contain optimal/Minimax play. A model trained on this data will learn only to avoid immediate mistakes but not to force a win/draw optimally.
- **No board rotation/augmentation:** All boards are in fixed orientation. You can apply data augmentation (rotations/reflections) during training to improve robustness.

## Citation

If you use this dataset, please cite:

```
@dataset{femto-xo,
  author    = {Abdelkader Hazerchi},
  title     = {FemtoXO Dataset: Synthetic Tic‑Tac‑Toe Games},
  year      = {2025},
  url       = {https://huggingface.co/datasets/abdelkader-dev/XO_matches}
}
```

## License

MIT – feel free to use, modify, and share.