Doctorslimm Kaushiks Criteria

---
name: doctorslimm-kaushiks-criteria
description: Compute DoctorSlimm/kaushiks_criteria via the HuggingFace `evaluate` library. Use when the user has predictions + references and wants the canonical implementation of DoctorSlimm/kaushiks_criteria.
metadata:
  skill_kind: metric
  source_lib: huggingface-evaluate
  hf_module: DoctorSlimm/kaushiks_criteria
  source: library_introspection
---

# doctorslimm-kaushiks-criteria

> Metric `DoctorSlimm/kaushiks_criteria` from the HuggingFace `evaluate` library.

## When to invoke

User asks to compute `DoctorSlimm/kaushiks_criteria` or wants HF evaluate's canonical version.

## Recipe

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

## Don'ts

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