diff --git a/ogb_examples/nodeproppred/unimp/main_arxiv.py b/ogb_examples/nodeproppred/unimp/main_arxiv.py
index b82742d72457783d2402826e412d24b1cd9fa2fe..f1ce99b61985d95fe649b3a102798cbc029ac4f7 100644
--- a/ogb_examples/nodeproppred/unimp/main_arxiv.py
+++ b/ogb_examples/nodeproppred/unimp/main_arxiv.py
@@ -20,7 +20,7 @@ evaluator = Evaluator(name='ogbn-arxiv')
 def get_config():
     parser = argparse.ArgumentParser()
     
-    ## 基本模型参数
+    ## model_base_arg
     model_group=parser.add_argument_group('model_base_arg')
     model_group.add_argument('--num_layers', default=3, type=int)
     model_group.add_argument('--hidden_size', default=128, type=int)
@@ -28,7 +28,7 @@ def get_config():
     model_group.add_argument('--dropout', default=0.3, type=float)
     model_group.add_argument('--attn_dropout', default=0, type=float)
     
-    ## label embedding模型参数
+    ## embed_arg
     embed_group=parser.add_argument_group('embed_arg')
     embed_group.add_argument('--use_label_e', action='store_true')
     embed_group.add_argument('--label_rate', default=0.625, type=float)
@@ -42,10 +42,6 @@ def get_config():
     train_group.add_argument('--log_file', default='result_arxiv.txt', type=str)
     return parser.parse_args()
 
-# def optimizer_func(lr=0.01):
-#     return F.optimizer.AdamOptimizer(learning_rate=lr, regularization=F.regularizer.L2Decay(
-#         regularization_coeff=0.001))
-
 def optimizer_func(lr=0.01):
     return F.optimizer.AdamOptimizer(learning_rate=lr, regularization=F.regularizer.L2Decay(
         regularization_coeff=0.0005))
@@ -81,17 +77,16 @@ def eval_test(parser, program, model, test_exe, graph, y_true, split_idx):
 
 def train_loop(parser, start_program, main_program, test_program, 
                model, graph, label, split_idx, exe, run_id, wf=None):
-    #启动上文构建的训练器
+    #start_program
     exe.run(start_program)
-    max_acc=0  # 最佳test_acc
-    max_step=0 # 最佳test_acc 对应step
-    max_val_acc=0 # 最佳val_acc
-    max_cor_acc=0 # 最佳val_acc对应test_acc
-    max_cor_step=0 # 最佳val_acc对应step
-    #训练循环
+    max_acc=0  # best test_acc
+    max_step=0 # step for best_test_acc
+    max_val_acc=0 # best val_acc
+    max_cor_acc=0 # test_acc for best_val_acc
+    max_cor_step=0 # step for test_acc
+    #training loop
     
-    for epoch_id in tqdm(range(parser.epochs)):
-        #运行训练器  
+    for epoch_id in tqdm(range(parser.epochs)):  
         
         if parser.use_label_e:
             feed_dict=model.gw.to_feed(graph)
@@ -115,7 +110,7 @@ def train_loop(parser, start_program, main_program, test_program,
 #         print(loss[1][0])
         loss = loss[0]
 
-        #测试结果
+        #test result
         result = eval_test(parser, test_program, model, exe, graph, label, split_idx)
         train_acc, valid_acc, test_acc = result
 
@@ -191,11 +186,7 @@ if __name__ == '__main__':
             test_prog=train_prog.clone(for_test=True)
             model.train_program()
             
-        
-#             ave_loss = train_program(pred_output)#训练程序
-#             lr, global_step= linear_warmup_decay(parser.lr, parser.epochs*0.1, parser.epochs)
-#             adam_optimizer = optimizer_func(lr)#训练优化函数
-            adam_optimizer = optimizer_func(parser.lr)#训练优化函数
+            adam_optimizer = optimizer_func(parser.lr)#adam_optimizer
             adam_optimizer.minimize(model.avg_cost)
     
     exe = F.Executor(place)
@@ -206,4 +197,4 @@ if __name__ == '__main__':
         total_test_acc+=train_loop(parser, startup_prog, train_prog, test_prog, model,
             graph, label, split_idx, exe, run_i, wf)
     wf.write(f'average: {100 * (total_test_acc/parser.runs):.2f}%')
-    wf.close()
\ No newline at end of file
+    wf.close()