Translationeditrate

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

# translationeditrate

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

## When to invoke this skill

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

## Reference signature

```python
from torchmetrics import TranslationEditRate

# _TranslationEditRate(normalize: bool = False, no_punctuation: bool = False, lowercase: bool = True, asian_support: bool = False, return_sentence_level_score: bool = False, **kwargs: Any) -> None
```

## Library docstring

```
Wrapper for deprecated import.

>>> preds = ['the cat is on the mat']
>>> target = [['there is a cat on the mat', 'a cat is on the mat']]
>>> ter = _TranslationEditRate()
>>> ter(preds, target)
tensor(0.1538)
```

## Quick recipe

```python
import torchmetrics as _m
score = _m.TranslationEditRate(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)`.