Charerrorrate

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

# charerrorrate

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

## When to invoke this skill

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

## Reference signature

```python
from torchmetrics import CharErrorRate

# _CharErrorRate(**kwargs: Any) -> None
```

## Library docstring

```
Wrapper for deprecated import.

>>> preds = ["this is the prediction", "there is an other sample"]
>>> target = ["this is the reference", "there is another one"]
>>> cer = _CharErrorRate()
>>> cer(preds, target)
tensor(0.3415)
```

## Quick recipe

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