diff --git a/utils_nlp/bert/sequence_classification.py b/utils_nlp/bert/sequence_classification.py
index c5d4614..7ba75ee 100644
--- a/utils_nlp/bert/sequence_classification.py
+++ b/utils_nlp/bert/sequence_classification.py
@@ -22,6 +22,7 @@ class BERTSequenceClassifier:
 
     def __init__(self, language=Language.ENGLISH, num_labels=2, cache_dir="."):
         """Initializes the classifier and the underlying pretrained model.
+
         Args:
             language (Language, optional): The pretrained model's language.
                                            Defaults to Language.ENGLISH.
@@ -54,6 +55,7 @@ class BERTSequenceClassifier:
         verbose=True,
     ):
         """Fine-tunes the BERT classifier using the given training data.
+
         Args:
             token_ids (list): List of training token id lists.
             input_mask (list): List of input mask lists.
@@ -149,8 +151,16 @@ class BERTSequenceClassifier:
         del [x_batch, y_batch, mask_batch]
         torch.cuda.empty_cache()
 
-    def predict(self, token_ids, input_mask, num_gpus=None, batch_size=32):
+    def predict(
+        self,
+        token_ids,
+        input_mask,
+        num_gpus=None,
+        batch_size=32,
+        probabilities=False,
+    ):
         """Scores the given dataset and returns the predicted classes.
+
         Args:
             token_ids (list): List of training token lists.
             input_mask (list): List of input mask lists.
@@ -158,6 +168,9 @@ class BERTSequenceClassifier:
                                       If None is specified, all available GPUs
                                       will be used. Defaults to None.
             batch_size (int, optional): Scoring batch size. Defaults to 32.
+            probabilities: If true, the predicted probability distribution
+                is returned; otherwise, the predicted classes are returned.
+                Defaults to False.
         Returns:
             [ndarray]: Predicted classes.
         """
@@ -188,6 +201,10 @@ class BERTSequenceClassifier:
                 preds.append(p_batch.cpu().data.numpy())
                 if i % batch_size == 0:
                     pbar.update(batch_size)
-        preds = [x.argmax(1) for x in preds]
+
         preds = np.concatenate(preds)
-        return preds
+
+        if probabilities:
+            return nn.Softmax(dim=1)(torch.Tensor(preds)).numpy()
+        else:
+            return preds.argmax(axis=1)