diff --git a/autoPyTorch/data/tabular_feature_validator.py b/autoPyTorch/data/tabular_feature_validator.py
index 49d241daa..6b1631c0b 100644
--- a/autoPyTorch/data/tabular_feature_validator.py
+++ b/autoPyTorch/data/tabular_feature_validator.py
@@ -22,7 +22,7 @@
 
 
 def _create_column_transformer(
-    preprocessors: Dict[str],
+    preprocessors: Dict,
     numerical_columns: List[str],
     categorical_columns: List[str],
 ) -> ColumnTransformer:
@@ -329,14 +329,14 @@ def _get_columns_info(
         # Make sure each column is a valid type
         for i, column in enumerate(X.columns):
             column_dtype = self.dtypes[i]
-            if column_dtype.name in ['category', 'bool']:
+            if column_dtype in ['category', 'bool']:
                 categorical_columns.append(column)
                 feat_type.append('categorical')
             # Move away from np.issubdtype as it causes
             # TypeError: data type not understood in certain pandas types
             elif not is_numeric_dtype(column_dtype):
                 # TODO verify how would this happen when we always convert the object dtypes to category
-                if column_dtype.name == 'object':
+                if column_dtype == 'object':
                     raise ValueError(
                         "Input Column {} has invalid type object. "
                         "Cast it to a valid dtype before using it in AutoPyTorch. "
@@ -368,7 +368,7 @@ def _get_columns_info(
                         "Make sure your data is formatted in a correct way, "
                         "before feeding it to AutoPyTorch.".format(
                             column,
-                            column_dtype.name,
+                            column_dtype,
                         )
                     )
             else: