Extendededitdistance

---
name: extendededitdistance
description: Compute the ExtendedEditDistance metric — provided by torchmetrics. Use when the user has predictions and ground-truth and needs to compute ExtendedEditDistance, or asks how to score with ExtendedEditDistance.
metadata:
  skill_kind: metric
  source_lib: torchmetrics
  import_path: torchmetrics.ExtendedEditDistance
  source: library_introspection
---

# extendededitdistance

> Metric `ExtendedEditDistance` from `torchmetrics` (torchmetrics.ExtendedEditDistance)

## When to invoke this skill

The user has predictions + ground truth and asks to evaluate with ExtendedEditDistance, or
mentions `torchmetrics.ExtendedEditDistance` directly, or wants the standard torchmetrics implementation.

## Reference signature

```python
from torchmetrics import ExtendedEditDistance

# _ExtendedEditDistance(language: Literal['en', 'ja'] = 'en', return_sentence_level_score: bool = False, alpha: float = 2.0, rho: float = 0.3, deletion: float = 0.2, insertion: float = 1.0, **kwargs: Any) -> None
```

## Library docstring

```
Wrapper for deprecated import.

>>> preds = ["this is the prediction", "here is an other sample"]
>>> target = ["this is the reference", "here is another one"]
>>> eed = _ExtendedEditDistance()
>>> eed(preds=preds, target=target)
tensor(0.3078)
```

## Quick recipe

```python
import torchmetrics as _m
score = _m.ExtendedEditDistance(y_true, y_pred)
```

## Don'ts

- Don't reimplement when the library version handles edge cases (NaN, ties, empty inputs) better than a hand-rolled formula.
- Always check the library version's argument order — sklearn is `(y_true, y_pred)` while torchmetrics is `(preds, target)`.