diff --git a/README.md b/README.md
index 0f11bd35..a866454f 100644
--- a/README.md
+++ b/README.md
@@ -4,6 +4,8 @@
 
 This is the PyTorch implementation of the [RotatE](https://openreview.net/forum?id=HkgEQnRqYQ) model for knowledge graph embedding (KGE). We provide a toolkit that gives state-of-the-art performance of several popular KGE models. The toolkit is quite efficient, which is able to train a large KGE model within a few hours on a single GPU.
 
+A faster multi-GPU implementation of RotatE and other KGE models is available in [GraphVite](https://github.com/DeepGraphLearning/graphvite).
+
 **Implemented features**
 
 Models:
diff --git a/codes/dataloader.py b/codes/dataloader.py
index 70d43a25..ed3f3492 100644
--- a/codes/dataloader.py
+++ b/codes/dataloader.py
@@ -59,8 +59,8 @@ def __getitem__(self, idx):
         
         negative_sample = np.concatenate(negative_sample_list)[:self.negative_sample_size]
 
-        negative_sample = torch.from_numpy(negative_sample)
-        
+        negative_sample = torch.LongTensor(negative_sample)
+
         positive_sample = torch.LongTensor(positive_sample)
             
         return positive_sample, negative_sample, subsampling_weight, self.mode
@@ -181,4 +181,4 @@ def one_shot_iterator(dataloader):
         '''
         while True:
             for data in dataloader:
-                yield data
\ No newline at end of file
+                yield data
diff --git a/codes/run.py b/codes/run.py
index 9cc7d2e9..457c6fdf 100644
--- a/codes/run.py
+++ b/codes/run.py
@@ -284,7 +284,6 @@ def main(args):
     
     logging.info('Start Training...')
     logging.info('init_step = %d' % init_step)
-    logging.info('learning_rate = %d' % current_learning_rate)
     logging.info('batch_size = %d' % args.batch_size)
     logging.info('negative_adversarial_sampling = %d' % args.negative_adversarial_sampling)
     logging.info('hidden_dim = %d' % args.hidden_dim)
@@ -296,6 +295,8 @@ def main(args):
     # Set valid dataloader as it would be evaluated during training
     
     if args.do_train:
+        logging.info('learning_rate = %d' % current_learning_rate)
+
         training_logs = []
         
         #Training Loop
@@ -357,4 +358,4 @@ def main(args):
         log_metrics('Test', step, metrics)
         
 if __name__ == '__main__':
-    main(parse_args())
\ No newline at end of file
+    main(parse_args())