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