Jpxkqx Peak Signal To Noise Ratio

---
name: jpxkqx-peak-signal-to-noise-ratio
description: Compute jpxkqx/peak_signal_to_noise_ratio via the HuggingFace `evaluate` library. Use when the user has predictions + references and wants the canonical implementation of jpxkqx/peak_signal_to_noise_ratio.
metadata:
  skill_kind: metric
  source_lib: huggingface-evaluate
  hf_module: jpxkqx/peak_signal_to_noise_ratio
  source: library_introspection
---

# jpxkqx-peak-signal-to-noise-ratio

> Metric `jpxkqx/peak_signal_to_noise_ratio` from the HuggingFace `evaluate` library.

## When to invoke

User asks to compute `jpxkqx/peak_signal_to_noise_ratio` or wants HF evaluate's canonical version.

## Recipe

```python
import evaluate
metric = evaluate.load("jpxkqx/peak_signal_to_noise_ratio")
result = metric.compute(predictions=preds, references=refs)
print(result)
```

## Don'ts

- Don't assume your in-house `jpxkqx/peak_signal_to_noise_ratio` matches HF — version conventions vary.
- Many evaluate metrics have task-specific arguments (`average=`, `lang=`, `model_type=`); read the metric card before reporting numbers.