# Rougescore

> Compute the ROUGEScore metric — provided by torchmetrics. Use when the user has predictions and ground-truth and needs to compute ROUGEScore, or asks how to score with ROUGEScore.

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

---


# rougescore

> Metric `ROUGEScore` from `torchmetrics` (torchmetrics.text.ROUGEScore)

## When to invoke this skill

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

## Reference signature

```python
from torchmetrics.text import ROUGEScore

# ROUGEScore(use_stemmer: bool = False, normalizer: Optional[Callable[[str], str]] = None, tokenizer: Optional[Callable[[str], collections.abc.Sequence[str]]] = None, accumulate: Literal['avg', 'best'] = 'best', rouge_keys: Union[str, tuple[str, ...]] = ('rouge1', 'rouge2', 'rougeL', 'rougeLsum'), **kwargs: Any) -> None
```

## Library docstring

```
`Calculate Rouge Score`_, used for automatic summarization.

This implementation should imitate the behaviour of the ``rouge-score`` package `Python ROUGE Implementation`

As input to ``forward`` and ``update`` the metric accepts the following input:

- ``preds`` (:class:`~Sequence`): An iterable of predicted sentences or a single predicted sentence
- ``target`` (:class:`~Sequence`): An iterable of target sentences
  or an iterable of interables of target sentences
  or a single target sentence

As output of ``forward`` and ``compute`` the metric returns the following output:

- ``rouge`` (:class:`~Dict`): A dictionary of tensor rouge scores for each input str rouge key

Args:
    use_stemmer: Use Porter stemmer to strip word suffixes to improve matching.
    normalizer: A user's own normalizer function.
        If this is ``None``, replacing any non-alpha-numeric characters with spaces is default.
        This function must take a ``str`` and return a ``str``.
    tokenizer:
        A user's own tokenizer function. If this is ``None``, splitting by spaces is default
        This function must take a ``str`` and return ``Sequence[str]``
    accumulate:
        Useful in case of multi-reference rouge score.

        - ``avg`` takes the avg of all references with respect to predictions
        - ``best`` takes the best fmeasure score obtained between prediction and multiple corresponding references.

    rouge_keys: A list of rouge types to calculate.
        Keys that are allowed are ``rougeL``, ``rougeLsum``, and ``rouge1`` through ``rouge9``.
    kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.

Example:
    >>> from torchmetrics.text.rouge import ROUGEScore
    >>> preds = "My name is John"
    >>> target = "Is your name John"
    >>> rouge = ROUGEScore()
    >>> from pprint import pprint
    >>> pprint(rouge(preds, target))
    {'rouge1_fmeasure': tensor(0.7500),
     'rouge1_precision': tensor(0.7500),
     'rouge1_recall': tensor(0.7500),
     'rouge2_fmeasure': tensor(0.),
     'rouge2_precision': tensor(0.),
     'rouge2_recall': tensor(0.),
     'rougeL_fmeasure': tensor(0.5000),
     'rougeL_precision': tensor(0.5000),
     
```

## Quick recipe

```python
import torchmetrics.text as _m
score = _m.ROUGEScore(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)`.