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Boolean

Use Boolean when your metric output is a binary decision (e.g., “toxic or not”, “adherent or not”). Set:
  • training.metric.type: "boolean"
  • Your dataset label column should be a binary value.
  • The training/evaluation pipeline converts labels to the correct target token.
Example: 
training:
  metric:
    type: "boolean"
  prompt_template: |
    Message:
    {input}

    Respond with "true" or "false".

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Categorical (multi-class)

Use multi-class when exactly one class should be selected for each example. Set:
  • training.metric.type: "multi_class"
  • training.metric.classes: ordered list of class names
The model is trained to output a single class key token (string index): "0", "1", … corresponding to your classes ordering. Example: 
training:
  metric:
    type: "multi_class"
    classes: ["neutral", "positive", "negative"]
  prompt_template: |
    {input}

    Respond with the class key like "0", "1", or "2".
  • Your dataset labels should be one of the class names (e.g. "joy")
  • The pipeline maps those to integer indices using training.metric.classes

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Not Supported

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