diff --git a/freqtrade/freqai/base_models/PyTorchModelTrainer.py b/freqtrade/freqai/base_models/PyTorchModelTrainer.py
index fc0a7600e..d02f1d896 100644
--- a/freqtrade/freqai/base_models/PyTorchModelTrainer.py
+++ b/freqtrade/freqai/base_models/PyTorchModelTrainer.py
@@ -104,8 +104,6 @@ class PyTorchModelTrainer:
                 .long()
                 .view(labels_view)
             )
-            # todo currently assuming class labels are strings ['0.0', '1.0' .. n_classes].
-            #  need to resolve it per ClassifierModel
 
             data_loader = DataLoader(
                 dataset,
diff --git a/freqtrade/freqai/prediction_models/PyTorchClassifierMultiTarget.py b/freqtrade/freqai/prediction_models/PyTorchClassifierMultiTarget.py
index aead0e46c..e58fa9cff 100644
--- a/freqtrade/freqai/prediction_models/PyTorchClassifierMultiTarget.py
+++ b/freqtrade/freqai/prediction_models/PyTorchClassifierMultiTarget.py
@@ -24,16 +24,18 @@ class PyTorchClassifierMultiTarget(BasePyTorchModel):
 
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
-        self.multiclass_names = self.freqai_info["multiclass_target_names"]
+        self.multiclass_names = self.freqai_info.get("multiclass_target_names", None)
+        logger.info(f"setting multiclass_names: {self.multiclass_names}")
         if not self.multiclass_names:
             raise OperationalException(
-                "Missing 'multiclass_names' in freqai_info,"
-                " multi class pytorch model requires predefined list of"
-                " class names matching the strategy being used"
+                "Missing 'multiclass_names' in freqai_info, "
+                "multi class pytorch classifier model requires predefined list of "
+                "class names matching the strategy being used."
             )
 
         self.class_name_to_index = {s: i for i, s in enumerate(self.multiclass_names)}
         self.index_to_class_name = {i: s for i, s in enumerate(self.multiclass_names)}
+        logger.info(f"class_name_to_index: {self.class_name_to_index}")
 
         model_training_parameters = self.freqai_info["model_training_parameters"]
         self.n_hidden = model_training_parameters.get("n_hidden", 1024)
@@ -48,7 +50,6 @@ class PyTorchClassifierMultiTarget(BasePyTorchModel):
         :param tensor_dictionary: the dictionary constructed by DataHandler to hold
                                 all the training and test data/labels.
         """
-
         self.encode_classes_name(data_dictionary, dk)
         n_features = data_dictionary['train_features'].shape[-1]
         model = PyTorchMLPModel(
@@ -124,9 +125,12 @@ class PyTorchClassifierMultiTarget(BasePyTorchModel):
         non_defined_labels = set(labels) - set(self.multiclass_names)
         if len(non_defined_labels) != 0:
             raise OperationalException(
-                f"Found non defined labels {non_defined_labels} ",
-                f"expecting labels {self.multiclass_names}"
+                f"Found non defined labels: {non_defined_labels}, ",
+                f"expecting labels: {self.multiclass_names}"
             )
 
-    def decode_classes_name(self, classes: List[int]) -> List[str]:
-        return list(map(lambda x: self.index_to_class_name[x], classes))
\ No newline at end of file
+    def decode_classes_name(self, classes: torch.Tensor[int]) -> List[str]:
+        """
+        decode class name int -> str
+        """
+        return list(map(lambda x: self.index_to_class_name[x.item()], classes))