src/evaluate/evaluator/audio_classification.py [92:117]:
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        super().__init__(task, default_metric_name=default_metric_name)

    def predictions_processor(self, predictions, label_mapping):
        pred_label = [max(pred, key=lambda x: x["score"])["label"] for pred in predictions]
        pred_label = [label_mapping[pred] if label_mapping is not None else pred for pred in pred_label]

        return {"predictions": pred_label}

    @add_start_docstrings(EVALUTOR_COMPUTE_START_DOCSTRING)
    @add_end_docstrings(EVALUATOR_COMPUTE_RETURN_DOCSTRING, TASK_DOCUMENTATION)
    def compute(
        self,
        model_or_pipeline: Union[
            str, "Pipeline", Callable, "PreTrainedModel", "TFPreTrainedModel"  # noqa: F821
        ] = None,
        data: Union[str, Dataset] = None,
        subset: Optional[str] = None,
        split: Optional[str] = None,
        metric: Union[str, EvaluationModule] = None,
        tokenizer: Optional[Union[str, "PreTrainedTokenizer"]] = None,  # noqa: F821
        feature_extractor: Optional[Union[str, "FeatureExtractionMixin"]] = None,  # noqa: F821
        strategy: Literal["simple", "bootstrap"] = "simple",
        confidence_level: float = 0.95,
        n_resamples: int = 9999,
        device: int = None,
        random_state: Optional[int] = None,
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src/evaluate/evaluator/image_classification.py [60:85]:
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        super().__init__(task, default_metric_name=default_metric_name)

    def predictions_processor(self, predictions, label_mapping):
        pred_label = [max(pred, key=lambda x: x["score"])["label"] for pred in predictions]
        pred_label = [label_mapping[pred] if label_mapping is not None else pred for pred in pred_label]

        return {"predictions": pred_label}

    @add_start_docstrings(EVALUTOR_COMPUTE_START_DOCSTRING)
    @add_end_docstrings(EVALUATOR_COMPUTE_RETURN_DOCSTRING, TASK_DOCUMENTATION)
    def compute(
        self,
        model_or_pipeline: Union[
            str, "Pipeline", Callable, "PreTrainedModel", "TFPreTrainedModel"  # noqa: F821
        ] = None,
        data: Union[str, Dataset] = None,
        subset: Optional[str] = None,
        split: Optional[str] = None,
        metric: Union[str, EvaluationModule] = None,
        tokenizer: Optional[Union[str, "PreTrainedTokenizer"]] = None,  # noqa: F821
        feature_extractor: Optional[Union[str, "FeatureExtractionMixin"]] = None,  # noqa: F821
        strategy: Literal["simple", "bootstrap"] = "simple",
        confidence_level: float = 0.95,
        n_resamples: int = 9999,
        device: int = None,
        random_state: Optional[int] = None,
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