README.md

---
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
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  - 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