Chrfscore

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

# chrfscore

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

## When to invoke this skill

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

## Reference signature

```python
from torchmetrics import CHRFScore

# _CHRFScore(n_char_order: int = 6, n_word_order: int = 2, beta: float = 2.0, lowercase: bool = False, whitespace: 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']]
>>> chrf = _CHRFScore()
>>> chrf(preds, target)
tensor(0.8640)
```

## Quick recipe

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