diff --git a/src/evaluate.py b/src/evaluate.py
index dec9ed0..6163178 100644
--- a/src/evaluate.py
+++ b/src/evaluate.py
@@ -76,6 +76,9 @@ def evaluate(cfg: DictConfig) -> Tuple[dict, dict]:
     log.info(f"Instantiating taskmodule <{cfg.taskmodule._target_}>")
     taskmodule: TaskModule = hydra.utils.instantiate(cfg.taskmodule, _convert_="partial")
 
+    # auto-convert the dataset if the taskmodule specifies a document type
+    dataset = taskmodule.convert_dataset(dataset)
+
     # Init pytorch-ie datamodule
     log.info(f"Instantiating datamodule <{cfg.datamodule._target_}>")
     datamodule: PieDataModule = hydra.utils.instantiate(
diff --git a/src/evaluate_documents.py b/src/evaluate_documents.py
index 0df8586..808acf2 100644
--- a/src/evaluate_documents.py
+++ b/src/evaluate_documents.py
@@ -70,6 +70,9 @@ def evaluate_documents(cfg: DictConfig) -> Tuple[dict, dict]:
     log.info(f"Instantiating metric <{cfg.metric._target_}>")
     metric: DocumentMetric = hydra.utils.instantiate(cfg.metric, _convert_="partial")
 
+    # auto-convert the dataset if the metric specifies a document type
+    dataset = metric.convert_dataset(dataset)
+
     # Init lightning loggers
     loggers = utils.instantiate_dict_entries(cfg, "logger")
 
diff --git a/src/predict.py b/src/predict.py
index d05c8cd..d65ee6b 100644
--- a/src/predict.py
+++ b/src/predict.py
@@ -84,6 +84,9 @@ def predict(cfg: DictConfig) -> Tuple[dict, dict]:
                 pipeline.device
             )
 
+        # auto-convert the dataset if the taskmodule specifies a document type
+        dataset = pipeline.taskmodule.convert_dataset(dataset)
+
     # Init the serializer
     serializer: Optional[DocumentSerializer] = None
     if cfg.get("serializer") and cfg.serializer.get("_target_"):
diff --git a/src/train.py b/src/train.py
index 4411264..8e8c836 100644
--- a/src/train.py
+++ b/src/train.py
@@ -107,23 +107,7 @@ def train(cfg: DictConfig) -> Tuple[dict, dict]:
     )
 
     # auto-convert the dataset if the taskmodule specifies a document type
-    if taskmodule.document_type is not None:
-        if issubclass(dataset.document_type, taskmodule.document_type):
-            log.info(
-                f"the dataset is already of the document type that is specified by the taskmodule: "
-                f"{taskmodule.document_type}"
-            )
-        else:
-            log.info(
-                f"convert the dataset to the document type that is specified by the taskmodule: "
-                f"{taskmodule.document_type}"
-            )
-            dataset = dataset.to_document_type(taskmodule.document_type)
-    else:
-        log.warning(
-            "The taskmodule does not specify a document type. The dataset can not be automatically converted "
-            "to a document type."
-        )
+    dataset = taskmodule.convert_dataset(dataset)
 
     # Init pytorch-ie datamodule
     log.info(f"Instantiating datamodule <{cfg.datamodule._target_}>")