---
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: 61786821797
    num_examples: 442684690
  download_size: 22010230074
  dataset_size: 61786821797
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

**173,866,932 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 **442,684,690 rows**.

This dataset is updated monthly, and was last updated on January 5th, 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.