# Translationeditrate

> 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.

- Skill: `qhjqhj00/translationeditrate` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/translationeditrate`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/translationeditrate/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/translationeditrate

---


# 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)`.