# Chrfscore

> 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.

- Skill: `qhjqhj00/chrfscore` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/chrfscore`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/chrfscore/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/chrfscore

---


# 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)`.