diff --git a/refactor/main.py b/refactor/main.py
index a1f5eef..027649b 100644
--- a/refactor/main.py
+++ b/refactor/main.py
@@ -9,7 +9,7 @@ from time import time
 
 
 def main(args):
-    OPTIMC = True # TODO
+    OPTIMC = False   # TODO
     N_JOBS = 8
     print('Running refactored...')
 
@@ -20,6 +20,7 @@ def main(args):
     EMBEDDINGS_PATH = '/home/andreapdr/gfun/embeddings'
     data = MultilingualDataset.load(_DATASET)
     data.set_view(languages=['it', 'fr'])
+    data.show_dimensions()
     lX, ly = data.training()
     lXte, lyte = data.test()
 
@@ -53,8 +54,8 @@ def main(args):
     # Init DocEmbedderList
     docEmbedders = DocEmbedderList(embedder_list=embedder_list, probabilistic=True)
     meta_parameters = None if not OPTIMC else [{'C': [1, 1e3, 1e2, 1e1, 1e-1]}]
-    meta = MetaClassifier(meta_learner=get_learner(calibrate=False, kernel='rbf', C=meta_parameters),
-                          meta_parameters=get_params(optimc=True))
+    meta = MetaClassifier(meta_learner=get_learner(calibrate=False, kernel='rbf'),
+                          meta_parameters=get_params(optimc=OPTIMC))
 
     # Init Funnelling Architecture
     gfun = Funnelling(first_tier=docEmbedders, meta_classifier=meta)
@@ -71,7 +72,7 @@ def main(args):
     print('\n[Testing Generalized Funnelling]')
     time_te = time()
     ly_ = gfun.predict(lXte)
-    l_eval = evaluate(ly_true=ly, ly_pred=ly_)
+    l_eval = evaluate(ly_true=lyte, ly_pred=ly_)
     time_te = round(time() - time_te, 3)
     print(f'Testing completed in {time_te} seconds!')
 
diff --git a/refactor/view_generators.py b/refactor/view_generators.py
index ca4ff93..579b8f1 100644
--- a/refactor/view_generators.py
+++ b/refactor/view_generators.py
@@ -55,7 +55,7 @@ class VanillaFunGen(ViewGen):
         self.vectorizer = TfidfVectorizerMultilingual(sublinear_tf=True, use_idf=True)
 
     def fit(self, lX, lY):
-        print('# Fitting VanillaFunGen...')
+        print('# Fitting VanillaFunGen (X)...')
         lX = self.vectorizer.fit_transform(lX)
         self.doc_projector.fit(lX, lY)
         return self
@@ -85,7 +85,7 @@ class MuseGen(ViewGen):
         self.vectorizer = TfidfVectorizerMultilingual(sublinear_tf=True, use_idf=True)
 
     def fit(self, lX, ly):
-        print('# Fitting MuseGen...')
+        print('# Fitting MuseGen (M)...')
         self.vectorizer.fit(lX)
         self.langs = sorted(lX.keys())
         self.lMuse = MuseLoader(langs=self.langs, cache=self.muse_dir)
@@ -120,7 +120,7 @@ class WordClassGen(ViewGen):
         self.vectorizer = TfidfVectorizerMultilingual(sublinear_tf=True, use_idf=True)
 
     def fit(self, lX, ly):
-        print('# Fitting WordClassGen...')
+        print('# Fitting WordClassGen (W)...')
         lX = self.vectorizer.fit_transform(lX)
         self.langs = sorted(lX.keys())
         wce = Parallel(n_jobs=self.n_jobs)(
@@ -207,7 +207,7 @@ class RecurrentGen(ViewGen):
         :param ly:
         :return:
         """
-        print('# Fitting RecurrentGen...')
+        print('# Fitting RecurrentGen (G)...')
         recurrentDataModule = RecurrentDataModule(self.multilingualIndex, batchsize=self.batch_size)
         trainer = Trainer(gradient_clip_val=1e-1, gpus=self.gpus, logger=self.logger, max_epochs=self.nepochs,
                           checkpoint_callback=False)
@@ -260,7 +260,7 @@ class BertGen(ViewGen):
         return BertModel(output_size=output_size, stored_path=self.stored_path, gpus=self.gpus)
 
     def fit(self, lX, ly):
-        print('# Fitting BertGen...')
+        print('# Fitting BertGen (M)...')
         self.multilingualIndex.train_val_split(val_prop=0.2, max_val=2000, seed=1)
         bertDataModule = BertDataModule(self.multilingualIndex, batchsize=self.batch_size, max_len=512)
         trainer = Trainer(gradient_clip_val=1e-1, max_epochs=self.nepochs, gpus=self.gpus,