diff --git a/README.md b/README.md
index a3f4878d..7c8c93dd 100644
--- a/README.md
+++ b/README.md
@@ -48,4 +48,5 @@ Haw-Shiuan Chang, Nikhil Agarwal, and Andrew McCallum. 2024. To Copy, or not to
 ```
 
 ## License
-The code from Recbole is under  [MIT License](https://github.com/RUCAIBox/RecBole/blob/master/LICENSE) but our modification (e.g., Softmax-CPR implementation) is under Apache-2.0 license. 
+All RecBole data and code are under [MIT License](https://github.com/RUCAIBox/RecBole/blob/master/LICENSE) and can only be used for academic purposes (as indicated in https://github.com/RUCAIBox/RecBole#license on 10/25/2023).
+Our modification  (e.g., Softmax-CPR implementation) is under Apache-2.0 license and could be used for commercial purpose.
diff --git a/recbole/model/sequential_recommender/gru4rec_ours.py b/recbole/model/sequential_recommender/gru4rec_ours.py
index 0c1e85b4..9959c47d 100644
--- a/recbole/model/sequential_recommender/gru4rec_ours.py
+++ b/recbole/model/sequential_recommender/gru4rec_ours.py
@@ -459,7 +459,7 @@ def calculate_loss_prob(self, interaction):
             #logits = torch.matmul(seq_output, test_item_emb.transpose(0, 1))
             #loss = self.loss_fct(logits, pos_items)
             #return loss
-            return loss, prediction_prob.squeeze(dim=0)
+            return loss, prediction_prob.squeeze(dim=1)
 
     def calculate_loss(self, interaction):
         loss, prediction_prob = self.calculate_loss_prob(interaction)
diff --git a/recbole/model/sequential_recommender/sasrec.py b/recbole/model/sequential_recommender/sasrec.py
index 41c5bffc..272d1cfb 100644
--- a/recbole/model/sequential_recommender/sasrec.py
+++ b/recbole/model/sequential_recommender/sasrec.py
@@ -471,7 +471,7 @@ def calculate_loss_prob(self, interaction):
                 raise Exception("Labels can not be None")
             #logits = torch.matmul(seq_output, test_item_emb.transpose(0, 1))
             #loss = self.loss_fct(logits, pos_items)
-            return loss, prediction_prob.squeeze(dim=0)
+            return loss, prediction_prob.squeeze(dim=1)
     
     def calculate_loss(self, interaction):
         loss, prediction_prob = self.calculate_loss_prob(interaction)