src/fmeval/eval_algorithms/classification_accuracy_semantic_robustness.py [155:181]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
            mean_delta_scores,
        ]
        pipeline = TransformPipeline(transforms)
        return pipeline

    def evaluate_sample(
        self,
        model_input: str,
        target_output: str,
        model: ModelRunner,
        prompt_template: str = DEFAULT_PROMPT_TEMPLATE,
    ) -> List[EvalScore]:
        """Compute classification accuracy semantic robustness metrics for a single sample.

        A sample is defined as a model input and target output pair.

        :param model_input: Text input, which will be composed into a prompt that gets fed to the model.
        :param target_output: The expected response from the model.
        :param model: An instance of ModelRunner representing the model under evaluation.
        :param prompt_template: A template used to compose the prompt from `model_input`.
        :return: A list of EvalScores.
        """
        sample = {
            DatasetColumns.MODEL_INPUT.value.name: model_input,
            DatasetColumns.TARGET_OUTPUT.value.name: target_output,
        }
        invoke_model = create_model_invocation_pipeline(model, prompt_template)
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



src/fmeval/eval_algorithms/summarization_accuracy_semantic_robustness.py [197:223]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
            mean_delta_scores,
        ]
        pipeline = TransformPipeline(transforms)
        return pipeline

    def evaluate_sample(
        self,
        model_input: str,
        target_output: str,
        model: ModelRunner,
        prompt_template: str = DEFAULT_PROMPT_TEMPLATE,
    ) -> List[EvalScore]:
        """Compute summarization accuracy semantic robustness metrics for a single sample.

        A sample is defined as a model input and model output pair.

        :param model_input: Text input, which will be composed into a prompt that gets fed to the model.
        :param target_output: The expected response from the model.
        :param model: An instance of ModelRunner representing the model under evaluation.
        :param prompt_template: A template used to compose the prompt from `model_input`.
        :return: A list of EvalScores.
        """
        sample = {
            DatasetColumns.MODEL_INPUT.value.name: model_input,
            DatasetColumns.TARGET_OUTPUT.value.name: target_output,
        }
        invoke_model = create_model_invocation_pipeline(model, prompt_template)
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -