diff --git a/main.py b/main.py
index da2748d..25d7a07 100644
--- a/main.py
+++ b/main.py
@@ -71,7 +71,7 @@ def main(args):
     print('\n[Testing Generalized Funnelling]')
     time_te = time.time()
     ly_ = gfun.predict(lXte)
-    l_eval = evaluate(ly_true=lyte, ly_pred=ly_)
+    l_eval = evaluate(ly_true=lyte, ly_pred=ly_, n_jobs=args.n_jobs)
     time_te = round(time.time() - time_te, 3)
     print(f'Testing completed in {time_te} seconds!')
 
diff --git a/src/view_generators.py b/src/view_generators.py
index e972ce7..a934181 100644
--- a/src/view_generators.py
+++ b/src/view_generators.py
@@ -18,6 +18,7 @@ This module contains the view generators that take care of computing the view sp
 from abc import ABC, abstractmethod
 # from time import time
 
+import torch
 from pytorch_lightning import Trainer
 from pytorch_lightning.loggers import TensorBoardLogger
 from pytorch_lightning.callbacks.early_stopping import EarlyStopping
@@ -241,6 +242,10 @@ class RecurrentGen(ViewGen):
         self.logger = TensorBoardLogger(save_dir='../tb_logs', name='rnn', default_hp_metric=False)
         self.early_stop_callback = EarlyStopping(monitor='val-macroF1', min_delta=0.00,
                                                  patience=self.patience, verbose=False, mode='max')
+
+        # modifying EarlyStopping global var in order to compute >= with respect to the best score
+        self.early_stop_callback.mode_dict['max'] = torch.ge
+
         self.lr_monitor = LearningRateMonitor(logging_interval='epoch')
 
     def _init_model(self):
@@ -348,6 +353,9 @@ class BertGen(ViewGen):
         self.early_stop_callback = EarlyStopping(monitor='val-macroF1', min_delta=0.00,
                                                  patience=self.patience, verbose=False, mode='max')
 
+        # modifying EarlyStopping global var in order to compute >= with respect to the best score
+        self.early_stop_callback.mode_dict['max'] = torch.ge
+
     def _init_model(self):
         output_size = self.multilingualIndex.get_target_dim()
         return BertModel(output_size=output_size, stored_path=self.stored_path, gpus=self.gpus)