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