diff --git a/src/pie_modules/taskmodules/metrics/wrapped_metric_with_unbatch_function.py b/src/pie_modules/taskmodules/metrics/wrapped_metric_with_unbatch_function.py
deleted file mode 100644
index a681d4213..000000000
--- a/src/pie_modules/taskmodules/metrics/wrapped_metric_with_unbatch_function.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import logging
-from typing import Any, Callable, Dict, Generic, List, Sequence, TypeVar, Union
-
-from torch import Tensor
-from torchmetrics import Metric
-
-logger = logging.getLogger(__name__)
-
-
-T = TypeVar("T")
-
-
-class WrappedMetricWithUnbatchFunction(Metric, Generic[T]):
-    """A wrapper around a metric that can be used with a batched input.
-
-    Args:
-        unbatch_function: A function that takes a batched input and returns an iterable of
-            individual inputs. This is used to unbatch the input before passing it to the wrapped
-            metric.
-        metric: The metric to wrap. It should be a subclass of torchmetrics.Metric.
-    """
-
-    def __init__(
-        self, unbatch_function: Callable[[T], Sequence[Any]], metric: Metric, **kwargs
-    ) -> None:
-        super().__init__(**kwargs)
-        self.unbatch_function = unbatch_function
-        self.metric = metric
-
-    def update(self, predictions: T, targets: T) -> None:
-        prediction_list = self.unbatch_function(predictions)
-        target_list = self.unbatch_function(targets)
-        if len(prediction_list) != len(target_list):
-            raise ValueError(
-                f"Number of predictions ({len(prediction_list)}) and targets ({len(target_list)}) do not match."
-            )
-        for prediction_str, target_str in zip(prediction_list, target_list):
-            self.metric(prediction_str, target_str)
-
-    def compute(self) -> Any:
-        return self.metric.compute()
-
-    def reset(self) -> None:
-        self.metric.reset()
-
-    @property
-    def metric_state(self) -> Dict[str, Union[List[Tensor], Tensor]]:
-        return self.metric.metric_state