# Exactmatch

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

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

---


# exactmatch

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

## When to invoke this skill

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

## Reference signature

```python
from torchmetrics import ExactMatch

# ExactMatch(task: Literal['binary', 'multiclass', 'multilabel'], threshold: float = 0.5, num_classes: Optional[int] = None, num_labels: Optional[int] = None, multidim_average: Literal['global', 'samplewise'] = 'global', ignore_index: Optional[int] = None, validate_args: bool = True, **kwargs: Any) -> torchmetrics.metric.Metric
```

## Library docstring

```
Compute Exact match (also known as subset accuracy).

Exact Match is a stricter version of accuracy where all labels have to match exactly for the sample to be
correctly classified.

This module is a simple wrapper to get the task specific versions of this metric, which is done by setting the
``task`` argument to either ``'multiclass'`` or ``'multilabel'``. See the documentation of
:class:`~torchmetrics.classification.MulticlassExactMatch` and
:class:`~torchmetrics.classification.MultilabelExactMatch` for the specific details of each argument influence and
examples.

Legacy Example:
    >>> from torch import tensor
    >>> target = tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]])
    >>> preds = tensor([[[0, 1], [2, 1], [0, 2]], [[2, 2], [2, 1], [1, 0]]])
    >>> metric = ExactMatch(task="multiclass", num_classes=3, multidim_average='global')
    >>> metric(preds, target)
    tensor(0.5000)

    >>> target = tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]])
    >>> preds = tensor([[[0, 1], [2, 1], [0, 2]], [[2, 2], [2, 1], [1, 0]]])
    >>> metric = ExactMatch(task="multiclass", num_classes=3, multidim_average='samplewise')
    >>> metric(preds, target)
    tensor([1., 0.])
```

## Quick recipe

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