Class EvalPrediction

src/transformers/src/transformers/trainer_utils.py:152–188

Evaluation output (always contains labels), to be used to compute metrics. Parameters: predictions (`np.ndarray`): Predictions of the model. label_ids (`np.ndarray`): Targets to be matched. inputs (`np.ndarray`, *optional*):

class EvalPrediction:
    """
    Evaluation output (always contains labels), to be used to compute metrics.

    Parameters:
        predictions (`np.ndarray`): Predictions of the model.
        label_ids (`np.ndarray`): Targets to be matched.
        inputs (`np.ndarray`, *optional*):
    """

    def __init__(
        self,
        predictions: Union[np.ndarray, Tuple[np.ndarray]],
        label_ids: Union[np.ndarray, Tuple[np.ndarray]],
        inputs: Optional[Union[np.ndarray, Tuple[np.ndarray]]] = None,
    ):
        self.predictions = predictions
        self.label_ids = label_ids
        self.inputs = inputs

    def __iter__(self):
        if self.inputs is not None:
            return iter((self.predictions, self.label_ids, self.inputs))
        else:
            return iter((self.predictions, self.label_ids))

    def __getitem__(self, idx):
        if idx < 0 or idx > 2:
            raise IndexError("tuple index out of range")
        if idx == 2 and self.inputs is None:
            raise IndexError("tuple index out of range")
        if idx == 0:
            return self.predictions
        elif idx == 1:
            return self.label_ids
        elif idx == 2:
            return self.inputs


class EvalLoopOutput(NamedTuple):