# Hpi Dhc Faireval

> Compute hpi-dhc/FairEval via the HuggingFace `evaluate` library. Use when the user has predictions + references and wants the canonical implementation of hpi-dhc/FairEval.

- Skill: `qhjqhj00/hpi-dhc-faireval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/hpi-dhc-faireval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/hpi-dhc-faireval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/hpi-dhc-faireval

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# hpi-dhc-faireval

> Metric `hpi-dhc/FairEval` from the HuggingFace `evaluate` library.

## When to invoke

User asks to compute `hpi-dhc/FairEval` or wants HF evaluate's canonical version.

## Recipe

```python
import evaluate
metric = evaluate.load("hpi-dhc/FairEval")
result = metric.compute(predictions=preds, references=refs)
print(result)
```

## Don'ts

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