|
--- |
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dataset_info: |
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features: |
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- name: state |
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dtype: string |
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- name: action |
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dtype: string |
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- name: future |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 184646417 |
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num_examples: 659576 |
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- name: test |
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num_bytes: 5755726 |
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num_examples: 62561 |
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download_size: 52539213 |
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dataset_size: 190402143 |
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license: apache-2.0 |
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--- |
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# Dataset Card for "chess10k" |
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## Dataset Details |
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This is a chess dataset created in the paper ["Implicit Search via Discrete Diffusion: A Study on Chess"](https://arxiv.org/abs/2502.19805). |
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`chess10k` contains 10k games from [Lichess](https://lichess.org/) and the actions are reannotated by [Stockfish 16](https://stockfishchess.org/). |
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The datasets contains three fields: |
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- `state`: the board state, which is represented in a FEN-like format. |
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- `action`: the best action, which is suggested by Stockfish 16 at the above state. |
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- `future`: the best future trajectory, which is suggested by Stockfish 16 followed by the above action. |
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## Citation |
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``` |
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@article{ye2025implicit, |
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title={Implicit Search via Discrete Diffusion: A Study on Chess}, |
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author={Ye, Jiacheng and Wu, Zhenyu and Gao, Jiahui and Wu, Zhiyong and Jiang, Xin and Li, Zhenguo and Kong, Lingpeng}, |
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journal={arXiv preprint arXiv:2502.19805}, |
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year={2025} |
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} |
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``` |
