diff --git a/training.py b/training.py
index 184fe54..60f63d0 100644
--- a/training.py
+++ b/training.py
@@ -46,9 +46,15 @@ if __name__ == '__main__':
     tokenizer = BertTokenizer.from_pretrained(args.bert_model_name)
 
     train_dataset = TokenizedDataFrameDataset(tokenizer, file_path=args.train_file)
+    val_dataset = TokenizedDataFrameDataset(tokenizer, file_path=args.train_file)
+    train_dataset.df = train_dataset.df.iloc[:-1000]
+    val_dataset.df = val_dataset.df.iloc[-1000:]
     test_dataset = TokenizedDataFrameDataset(tokenizer, file_path=args.test_file)
 
-    train_loader = DataLoader(train_dataset, shuffle=True, batch_size=args.batch_size, num_workers=args.n_workers)
+    train_loader = DataLoader(train_dataset, shuffle=True, batch_size=args.batch_size,
+                              num_workers=args.n_workers)
+    val_loader = DataLoader(val_dataset, batch_size=args.batch_size,
+                              num_workers=args.n_workers)
     test_loader = DataLoader(test_dataset, batch_size=args.batch_size, num_workers=args.n_workers)
 
     # Load pre-trained model (weights)
@@ -89,7 +95,7 @@ if __name__ == '__main__':
         model.eval()
         labels = []
         predictions = []
-        for batch in tqdm(test_loader):
+        for batch in tqdm(val_loader):
             optimizer.zero_grad()
 
             input_ids, input_mask, segment_ids = batch['x']
@@ -105,4 +111,25 @@ if __name__ == '__main__':
             predictions.append(torch.argmax(logits, dim=1))
         labels = torch.LongTensor(labels)
         predictions = torch.LongTensor(predictions)
-        print(f'Epoch: {i}\tTest Accuracy: {(labels == predictions).mean()}')
+        print(f'Epoch: {i}\tVal Accuracy: {(labels == predictions).mean()}')
+
+    model.eval()
+    labels = []
+    predictions = []
+    for batch in tqdm(test_loader):
+        optimizer.zero_grad()
+
+        input_ids, input_mask, segment_ids = batch['x']
+        input_ids = input_ids.to(device)
+        input_mask = input_mask.to(device)
+        segment_ids = segment_ids.to(device)
+
+        y = batch['y']
+        y = y.to(device)
+
+        loss, logits = model.forward(input_ids, input_mask, segment_ids, labels=y)
+        labels.append(torch.argmax(y, dim=1))
+        predictions.append(torch.argmax(logits, dim=1))
+    labels = torch.LongTensor(labels)
+    predictions = torch.LongTensor(predictions)
+    print(f'Epoch: {i}\tTest Accuracy: {(labels == predictions).mean()}')