README.md

---
title: Levenshtein distance
emoji: ✍️
colorFrom: blue
colorTo: green
tags:
- evaluate
- metric
description: Levenshtein (edit) distance
sdk: gradio
sdk_version: 5.9.1
app_file: app.py
pinned: false
---

# Metric Card for the Levenshtein (edit) distance

## Metric Description

This metric computes the Levenshtein distance, also commonly called "edit distance". The Levenshtein distance measures the number of combined editions, deletions and additions to perform on a string so that it becomes identical to a second one. It is a popular metric for text similarity.
This module directly calls the [Levenshtein package](https://github.com/rapidfuzz/Levenshtein) for fast execution speed.

## How to Use

### Inputs

*List all input arguments in the format below*
- **predictions** *(string): sequence of prediction strings*
- **references** *(string): sequence of reference string;*
- **kwargs** *keyword arguments to pass to the [Levenshtein.distance](https://rapidfuzz.github.io/Levenshtein/levenshtein.html#Levenshtein.distance) method.*

### Output Values

Dictionary mapping to the average Levenshtein distance (lower is better) and the ratio ([0, 1]) distance (higher is better).

### Examples

```Python
import evaluate

levenshtein = evaluate.load("Natooz/Levenshtein")
results = levenshtein.compute(
    predictions=[
        "foo", "baroo"  # 0 and 2 edits
    ],
    references=[
        "foo", "bar"
    ],
)
print(results)
# {"levenshtein": 1, "levenshtein_ratio": 0.875}
```

## Citation

```bibtex
@ARTICLE{1966SPhD...10..707L,
       author = {{Levenshtein}, V.~I.},
        title = "{Binary Codes Capable of Correcting Deletions, Insertions and Reversals}",
      journal = {Soviet Physics Doklady},
         year = 1966,
        month = feb,
       volume = {10},
        pages = {707},
       adsurl = {https://ui.adsabs.harvard.edu/abs/1966SPhD...10..707L},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
```