Leslyarun Fbeta Score

---
name: leslyarun-fbeta-score
description: Compute leslyarun/fbeta_score via the HuggingFace `evaluate` library. Use when the user has predictions + references and wants the canonical implementation of leslyarun/fbeta_score.
metadata:
  skill_kind: metric
  source_lib: huggingface-evaluate
  hf_module: leslyarun/fbeta_score
  source: library_introspection
---

# leslyarun-fbeta-score

> Metric `leslyarun/fbeta_score` from the HuggingFace `evaluate` library.

## When to invoke

User asks to compute `leslyarun/fbeta_score` or wants HF evaluate's canonical version.

## Recipe

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

## Don'ts

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