score

dialz.score.get_activation_score(input_text, model, control_vector, layer_index=None, scoring_method='mean')

Compute the activation score for input_text by projecting hidden states onto the control_vector direction(s) at the specified layer(s).

Scoring methods:

• ‘mean’: Average dot products over all tokens.

• ‘final_token’: Dot product of the final token.

• ‘max_token’: Maximum dot product among tokens.

• ‘median_token’: Median dot product among tokens.

Parameters:

• input_text (str) – The input string to evaluate.

• model (SteeringModel) – The model to use for computing activations.

• control_vector (SteeringVector) – Contains direction(s) keyed by layer index.

• layer_index (int or list[int], optional) – Layer(s) to use. Defaults to last in model.layer_ids.

• scoring_method (str) – Scoring method to use.

Return type:

tuple[float, int, list[float]]

Returns:

A tuple containing:

• score: Averaged activation score across selected layers.

• token_len: Number of tokens in the input.

• unaggregated_scores: Unaggregated dot product scores for each layer.