# Wordinfolost

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

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

---


# wordinfolost

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

## When to invoke this skill

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

## Reference signature

```python
from torchmetrics import WordInfoLost

# _WordInfoLost(**kwargs: Any) -> None
```

## Library docstring

```
Wrapper for deprecated import.

>>> preds = ["this is the prediction", "there is an other sample"]
>>> target = ["this is the reference", "there is another one"]
>>> wil = _WordInfoLost()
>>> wil(preds, target)
tensor(0.6528)
```

## Quick recipe

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