Compute the SQuAD metric — provided by torchmetrics. Use when the user has predictions and ground-truth and needs to compute SQuAD, or asks how to score with SQuAD.
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---
name: squad
description: Compute the SQuAD metric — provided by torchmetrics. Use when the user has predictions and ground-truth and needs to compute SQuAD, or asks how to score with SQuAD.
metadata:
skill_kind: metric
source_lib: torchmetrics
import_path: torchmetrics.SQuAD
source: library_introspection
---
# squad
> Metric `SQuAD` from `torchmetrics` (torchmetrics.SQuAD)
## When to invoke this skill
The user has predictions + ground truth and asks to evaluate with SQuAD, or
mentions `torchmetrics.SQuAD` directly, or wants the standard torchmetrics implementation.
## Reference signature
```python
from torchmetrics import SQuAD
# _SQuAD(**kwargs: Any) -> None
```
## Library docstring
```
Wrapper for deprecated import.
>>> preds = [{"prediction_text": "1976", "id": "56e10a3be3433e1400422b22"}]
>>> target = [{"answers": {"answer_start": [97], "text": ["1976"]}, "id": "56e10a3be3433e1400422b22"}]
>>> squad = _SQuAD()
>>> squad(preds, target)
{'exact_match': tensor(100.), 'f1': tensor(100.)}
```
## Quick recipe
```python
import torchmetrics as _m
score = _m.SQuAD(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)`.
