Compute buelfhood/fbeta_score via the HuggingFace `evaluate` library. Use when the user has predictions + references and wants the canonical implementation of buelfhood/fbeta_score.
Installs into .claude/skills of the current project.
Are you the author of Buelfhood Fbeta Score?
Add the live security badge to your README. It updates with every re-scan.
[](https://www.skillsdirectory.com/skills/qhjqhj00-buelfhood-fbeta-score)
---
name: buelfhood-fbeta-score
description: Compute buelfhood/fbeta_score via the HuggingFace `evaluate` library. Use when the user has predictions + references and wants the canonical implementation of buelfhood/fbeta_score.
metadata:
skill_kind: metric
source_lib: huggingface-evaluate
hf_module: buelfhood/fbeta_score
source: library_introspection
---
# buelfhood-fbeta-score
> Metric `buelfhood/fbeta_score` from the HuggingFace `evaluate` library.
## When to invoke
User asks to compute `buelfhood/fbeta_score` or wants HF evaluate's canonical version.
## Recipe
```python
import evaluate
metric = evaluate.load("buelfhood/fbeta_score")
result = metric.compute(predictions=preds, references=refs)
print(result)
```
## Don'ts
- Don't assume your in-house `buelfhood/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.
