Compute BucketHeadP65/roc_curve via the HuggingFace `evaluate` library. Use when the user has predictions + references and wants the canonical implementation of BucketHeadP65/roc_curve.
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---
name: bucketheadp65-roc-curve
description: Compute BucketHeadP65/roc_curve via the HuggingFace `evaluate` library. Use when the user has predictions + references and wants the canonical implementation of BucketHeadP65/roc_curve.
metadata:
skill_kind: metric
source_lib: huggingface-evaluate
hf_module: BucketHeadP65/roc_curve
source: library_introspection
---
# bucketheadp65-roc-curve
> Metric `BucketHeadP65/roc_curve` from the HuggingFace `evaluate` library.
## When to invoke
User asks to compute `BucketHeadP65/roc_curve` or wants HF evaluate's canonical version.
## Recipe
```python
import evaluate
metric = evaluate.load("BucketHeadP65/roc_curve")
result = metric.compute(predictions=preds, references=refs)
print(result)
```
## Don'ts
- Don't assume your in-house `BucketHeadP65/roc_curve` matches HF — version conventions vary.
- Many evaluate metrics have task-specific arguments (`average=`, `lang=`, `model_type=`); read the metric card before reporting numbers.
