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