Datasets:
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---
license: mit
size_categories:
- 10M<n<100M
task_categories:
- question-answering
- token-classification
pretty_name: Chess Evaluations
dataset_info:
- config_name: evals_large
features:
- name: FEN
dtype: string
- name: Evaluation
dtype: string
splits:
- name: train
num_bytes: 872492457
num_examples: 12954834
download_size: 334299450
dataset_size: 872492457
- config_name: mcts
features:
- name: fen
dtype: string
- name: node_data
list:
- name: move
dtype: string
- name: N
dtype: int64
- name: Q
dtype: float64
- name: D
dtype: float64
- name: P
dtype: float64
- name: edges
sequence:
sequence: int64
- name: graph_nodes
dtype: int64
- name: depth
dtype: int64
- name: seldepth
dtype: int64
- name: time
dtype: float64
- name: nodes
dtype: int64
- name: score
dtype: string
- name: nps
dtype: int64
- name: tbhits
dtype: int64
- name: pv
sequence: string
- name: move
dtype: string
- name: ponder
dtype: string
- name: draw_offered
dtype: bool
- name: resigned
dtype: bool
- name: limit
struct:
- name: time
dtype: int64
- name: depth
dtype: int64
- name: nodes
dtype: int64
splits:
- name: train
num_bytes: 48076633242
num_examples: 99907
download_size: 15234074915
dataset_size: 48076633242
- config_name: pretrain_conv
features:
- name: id
dtype: string
- name: state
dtype: string
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
splits:
- name: train
num_bytes: 3850440686
num_examples: 10000000
download_size: 636942361
dataset_size: 3850440686
- config_name: randoms
features:
- name: FEN
dtype: string
- name: Evaluation
dtype: string
splits:
- name: train
num_bytes: 71226739
num_examples: 1000273
download_size: 18919700
dataset_size: 71226739
- config_name: tactics
features:
- name: FEN
dtype: string
- name: Evaluation
dtype: string
- name: Move
dtype: string
splits:
- name: train
num_bytes: 192267899
num_examples: 2628219
download_size: 92596702
dataset_size: 192267899
configs:
- config_name: evals_large
data_files:
- split: train
path: evals_large/train-*
- config_name: mcts
data_files:
- split: train
path: mcts/train-*
- config_name: pretrain_conv
data_files:
- split: train
path: pretrain_conv/train-*
- config_name: randoms
data_files:
- split: train
path: randoms/train-*
- config_name: tactics
data_files:
- split: train
path: tactics/train-*
tags:
- rl
- chess
- reinforcement learning
---
# Chess Evaluations Dataset
This dataset contains chess positions represented in FEN (Forsyth-Edwards Notation) along with their evaluations and next moves for tactical evals. The dataset is divided into three configurations:
1. **tactics**: Includes chess positions, their evaluations, and the best move in the position.
2. **randoms**: Contains random chess positions and their evaluations.
3. **chess_data**: General chess positions with evaluations.
This is an in progress dataset which contains millions of positions with stockfish 11 (depth 22) evaluations. Please help contribute evaluations of the positions to the repo, the original owner of the dataset is [r2dev2](https://github.com/r2dev2/ChessData).
> ❗❗❗ Updates to the original dataset will be on the [version hosted on kaggle](https://www.kaggle.com/ronakbadhe/chess-evaluations).
## Dataset Structure
Each configuration can be loaded separately:
- **tactics**: Columns - `FEN`, `Evaluation`, `Move`
- **randoms**: Columns - `FEN`, `Evaluation`
- **chess_data**: Columns - `FEN`, `Evaluation`
## Usage
You can load each configuration using the `datasets` library:
```python
from datasets import load_dataset
# Load the tactics dataset
tactics_dataset = load_dataset("someshsingh22/chess-evaluations", "tactics")
# Load the randoms dataset
randoms_dataset = load_dataset("someshsingh22/chess-evaluations", "randoms")
```
## Contributing
To get started download a pre-built executable from the releases of [chess contributor](https://github.com/r2dev2bb8/ChessDataContributor/releases) and run it.
The evaluation should go in eval folder under same name |