Mdocekal Multi Label Precision Recall Accuracy Fscore

---
name: mdocekal-multi-label-precision-recall-accuracy-fscore
description: Compute mdocekal/multi_label_precision_recall_accuracy_fscore via the HuggingFace `evaluate` library. Use when the user has predictions + references and wants the canonical implementation of mdocekal/multi_label_precision_recall_accuracy_fscore.
metadata:
  skill_kind: metric
  source_lib: huggingface-evaluate
  hf_module: mdocekal/multi_label_precision_recall_accuracy_fscore
  source: library_introspection
---

# mdocekal-multi-label-precision-recall-accuracy-fscore

> Metric `mdocekal/multi_label_precision_recall_accuracy_fscore` from the HuggingFace `evaluate` library.

## When to invoke

User asks to compute `mdocekal/multi_label_precision_recall_accuracy_fscore` or wants HF evaluate's canonical version.

## Recipe

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

## Don'ts

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