diff --git a/enhancer/models/model.py b/enhancer/models/model.py
index 8e607ed..b1bdd86 100644
--- a/enhancer/models/model.py
+++ b/enhancer/models/model.py
@@ -1,3 +1,7 @@
+try:
+    from functools import cached_property
+except ImportError:
+    from backports.cached_property import cached_property
 from importlib import import_module
 from huggingface_hub import cached_download, hf_hub_url
 import logging
@@ -42,7 +46,34 @@ class Model(pl.LightningModule):
         if self.logger:
             self.logger.experiment.log_dict(dict(self.hparams),"hyperparameters.json")
         
-    
+        self.loss = loss
+        self.metric = metric
+
+    @property
+    def loss(self):
+        return self._loss
+        
+    @loss.setter
+    def loss(self,loss):
+
+        if isinstance(loss,str):
+                losses = [loss]
+            
+        self._loss = Avergeloss(losses)
+
+    @property
+    def metric(self):
+        return self._metric
+        
+    @metric.setter
+    def metric(self,metric):
+
+        if isinstance(metric,str):
+                metric = [metric]
+            
+        self._metric = Avergeloss(metric)
+
+
     @property
     def dataset(self):
         return self._dataset
@@ -55,16 +86,7 @@ class Model(pl.LightningModule):
         if stage == "fit":
             self.dataset.setup(stage)
             self.dataset.model = self
-            self.loss = self.setup_loss(self.hparams.loss) 
-            self.metric = self.setup_loss(self.hparams.metric)
-        
-    def setup_loss(self,loss):
-
-        if isinstance(loss,str):
-            losses = [loss]
-        
-        return Avergeloss(losses)
-
+         
     def train_dataloader(self):
         return self.dataset.train_dataloader()
 
@@ -224,7 +246,12 @@ class Model(pl.LightningModule):
             Inference.write_output(waveform,audio,model_sampling_rate)
 
         else:
-            return waveform            
+            return waveform    
+    
+    @property
+    def valid_monitor(self):
+
+        return "max" if self.loss.higher_better else "min"