def f1_score(self, scores: Union[List[float], List[List]], threshold=0.5):
        """
        Calculates the F1 score given true labels and model-generated scores.

        Parameters:
        - scores (list of float): A list of model-generated scores (0 to 1.0).
        - threshold (float): The threshold to convert scores to binary predictions. Default is 0.5.

        Returns:
        - float: The F1 score.
        """
        # Calculate precision and recall
        precision = self.precision(scores, threshold)
        recall = self.recall(scores, threshold)

        # Handle the case where both precision and recall are zero to avoid division by zero
        if precision + recall == 0:
            return 0.0  # or handle as needed (e.g., return None, raise an exception)

        # Calculate F1 score
        f1 = 2 * (precision * recall) / (precision + recall)
        return f1

        Calculates the F1 score given true labels and model-generated scores.

        Parameters:
        - scores (list of float): A list of model-generated scores (0 to 1.0).
        - threshold (float): The threshold to convert scores to binary predictions. Default is 0.5.

        Returns:
        - float: The F1 score.
        
src/feedback/trulens/feedback/groundtruth.py