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---
license: cc0-1.0
size_categories:
- 100M<n<1B
dataset_info:
features:
- name: fen
dtype: string
- name: line
dtype: string
- name: depth
dtype: int64
- name: knodes
dtype: int64
- name: cp
dtype: int64
- name: mate
dtype: int64
splits:
- name: train
num_bytes: 86266003148
num_examples: 618678212
download_size: 30329004321
dataset_size: 86266003148
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- chess
- stockfish
- lichess
- games
---
# Dataset Card for the Lichess Evaluations dataset
<!-- Provide a quick summary of the dataset. -->
## Dataset Description
**247,858,650 chess positions** evaluated with Stockfish at various depths and node count. Produced by, and for, the [Lichess analysis board](https://lichess.org/analysis), running various flavours of Stockfish within user browsers. This version of the dataset is a de-normalized version of [the original dataset](https://database.lichess.org/#evals) and contains **618,678,212 rows**.
This dataset is updated monthly, and was last updated on June 11th, 2025.
### Dataset Creation
```python
from datasets import load_dataset
dset = load_dataset("json", data_files="lichess_db_eval.jsonl", split="train")
def batch_explode_rows(batch):
exploded = {"fen": [], "line": [], "depth": [], "knodes": [], "cp": [], "mate": []}
for fen, evals in zip(batch["fen"], batch["evals"]):
for eval_ in evals:
for pv in eval_["pvs"]:
exploded["fen"].append(fen)
exploded["line"].append(pv["line"])
exploded["depth"].append(eval_["depth"])
exploded["knodes"].append(eval_["knodes"])
exploded["cp"].append(pv["cp"])
exploded["mate"].append(pv["mate"])
return exploded
dset = dset.map(batch_explode_rows, batched=True, batch_size=64, num_proc=12, remove_columns=dset.column_names)
dset.push_to_hub("Lichess/chess-evaluations")
```
### Dataset Usage
Using the `datasets` library:
```python
from datasets import load_dataset
dset = load_dataset("Lichess/chess-evaluations", split="train")
```
## Dataset Details
### Dataset Sample
One row of the dataset looks like this:
```python
{
"fen": "2bq1rk1/pr3ppn/1p2p3/7P/2pP1B1P/2P5/PPQ2PB1/R3R1K1 w - -",
"line": "g2e4 f7f5 e4b7 c8b7 f2f3 b7f3 e1e6 d8h4 c2h2 h4g4",
"depth": 36,
"knodes": 206765,
"cp": 311,
"mate": None
}
```
### Dataset Fields
Every row of the dataset contains the following fields:
- **`fen`**: `string`, the position FEN only contains pieces, active color, castling rights, and en passant square.
- **`line`**: `string`, the principal variation, in UCI format.
- **`depth`**: `string`, the depth reached by the engine.
- **`knodes`**: `int`, the number of kilo-nodes searched by the engine.
- **`cp`**: `int`, the position's centipawn evaluation. This is `None` if mate is certain.
- **`mate`**: `int`, the position's mate evaluation. This is `None` if mate is not certain. |