Skip to main content

​
Boolean

Use Boolean when your metric is a binary decision, such as toxicity or context adherence. Set metric.type: "binary" For binary metrics: We recommend you add llmaj_source_prompt so we can auto extract the below fields, but if you wish to have granular control then:
  • you must define exactly 2 class labels
  • labels are typically 0 and 1
  • each class label should include a name and rubric
Example: 
metric:
  type: "binary"
  class_labels:
    - name: "positive"
      label: 1
      rubric: "..."
    - name: "negative"
      label: 0
      rubric: "..."

​
Categorical (multi-class)

Use Categorical (multi-class) when exactly one class should be assigned to each example. For example tone Set:
  • metric.type: "multi-class"
For multi-class metrics: We recommend you add llmaj_source_prompt so we can auto extract the below fields, but if you wish to have granular control then:
  • define one class label entry per class
  • label values should be unique
  • in practice, sequential integer labels are the most common pattern
Example: 
metric:
  type: "multi-class"
  class_labels:
    - name: "neutral"
      label: 0
      rubric: "..."
    - name: "positive"
      label: 1
      rubric: "..."
    - name: "negative"
      label: 2
      rubric: "..."

​
Not Supported

These output types are not currently supported by Luna Studio -
  • Count
  • Discrete
  • Percentage
  • Multilabel