diff --git a/src/models/pl_bert.py b/src/models/pl_bert.py
index a9b669f..129c3b4 100644
--- a/src/models/pl_bert.py
+++ b/src/models/pl_bert.py
@@ -163,6 +163,11 @@ class BertModel(pl.LightningModule):
                     batch = pad(batch, pad_index=self.bert.config.pad_token_id, max_pad_length=max_pad_len)
                     batch = torch.LongTensor(batch).to('cuda' if self.gpus else 'cpu')
                     _, output = self.forward(batch)
+
+                    # deleting batch from gpu to avoid cuda OOM
+                    del batch
+                    torch.cuda.empty_cache()
+
                     doc_embeds = output[-1][:, 0, :]
                     l_embed[lang].append(doc_embeds.cpu())
             for k, v in l_embed.items():
diff --git a/src/view_generators.py b/src/view_generators.py
index b0f70bf..452714c 100644
--- a/src/view_generators.py
+++ b/src/view_generators.py
@@ -16,7 +16,7 @@ This module contains the view generators that take care of computing the view sp
 - View generator (-b): generates document embedding via mBERT model.
 """
 from abc import ABC, abstractmethod
-from time import time
+# from time import time
 
 from pytorch_lightning import Trainer
 from pytorch_lightning.loggers import TensorBoardLogger
@@ -27,6 +27,7 @@ from src.models.pl_bert import BertModel
 from src.models.pl_gru import RecurrentModel
 from src.util.common import TfidfVectorizerMultilingual, _normalize
 from src.util.embeddings_manager import MuseLoader, XdotM, wce_matrix
+# TODO: add early stop monitoring validation macroF1 + model checkpointing and loading from checkpoint
 
 
 class ViewGen(ABC):
@@ -293,10 +294,10 @@ class RecurrentGen(ViewGen):
         data = self.multilingualIndex.l_devel_index()
         self.model.to('cuda' if self.gpus else 'cpu')
         self.model.eval()
-        time_init = time.time()
+        # time_init = time.time()
         l_embeds = self.model.encode(data, l_pad, batch_size=256)
-        transform_time = round(time.time() - time_init, 3)
-        print(f'Executed! Transform took: {transform_time}')
+        # transform_time = round(time.time() - time_init, 3)
+        # print(f'Executed! Transform took: {transform_time}')
         return l_embeds
 
     def fit_transform(self, lX, ly):
@@ -362,10 +363,10 @@ class BertGen(ViewGen):
         data = tokenize(data, max_len=512)
         self.model.to('cuda' if self.gpus else 'cpu')
         self.model.eval()
-        time_init = time.time()
+        # time_init = time.time()
         l_emebds = self.model.encode(data, batch_size=64)
-        transform_time = round(time.time() - time_init, 3)
-        print(f'Executed! Transform took: {transform_time}')
+        # transform_time = round(time.time() - time_init, 3)
+        # print(f'Executed! Transform took: {transform_time}')
         return l_emebds
 
     def fit_transform(self, lX, ly):