diff --git a/python/examples/bert/bert_rpc_client.py b/python/examples/bert/bert_rpc_client.py
index a3a86aca72514d506572f1edb6a4697c19215de3..959143d1c5703bbcd1615e90414e5bd4327f0017 100644
--- a/python/examples/bert/bert_rpc_client.py
+++ b/python/examples/bert/bert_rpc_client.py
@@ -45,20 +45,6 @@ class BertService():
         self.pid = os.getpid()
         self.profile = True if ("FLAGS_profile_client" in os.environ and
                                 os.environ["FLAGS_profile_client"]) else False
-        '''
-        module = hub.Module(name=self.model_name)
-        inputs, outputs, program = module.context(
-            trainable=True, max_seq_len=self.max_seq_len)
-        input_ids = inputs["input_ids"]
-        position_ids = inputs["position_ids"]
-        segment_ids = inputs["segment_ids"]
-        input_mask = inputs["input_mask"]
-        self.reader = hub.reader.ClassifyReader(
-            vocab_path=module.get_vocab_path(),
-            dataset=None,
-            max_seq_len=self.max_seq_len,
-            do_lower_case=self.do_lower_case)
-        '''
         self.reader = BertReader(vocab_file="vocab.txt", max_seq_len=20)
         self.reader_flag = True
 
@@ -68,28 +54,8 @@ class BertService():
         self.client.connect(server_addr)
 
     def run_general(self, text, fetch):
-        '''
-        self.batch_size = len(text)
-        data_generator = self.reader.data_generator(
-            batch_size=self.batch_size, phase='predict', data=text)
-        '''
         result = []
         prepro_start = time.time()
-        '''
-        for run_step, batch in enumerate(data_generator(), start=1):
-            token_list = batch[0][0].reshape(-1).tolist()
-            pos_list = batch[0][1].reshape(-1).tolist()
-            sent_list = batch[0][2].reshape(-1).tolist()
-            mask_list = batch[0][3].reshape(-1).tolist()
-            for si in range(self.batch_size):
-                feed = {
-                    "input_ids": token_list,
-                    "position_ids": pos_list,
-                    "segment_ids": sent_list,
-                    "input_mask": mask_list
-                }
-                prepro_end = time.time()
-        '''
         feed = self.reader.process(text)
         if self.profile:
             print("PROFILE\tpid:{}\tbert_pre_0:{} bert_pre_1:{}".format(
@@ -102,33 +68,10 @@ class BertService():
 
     def run_batch_general(self, text, fetch):
         self.batch_size = len(text)
-        '''
-        data_generator = self.reader.data_generator(
-            batch_size=self.batch_size, phase='predict', data=text)
-        '''
         result = []
         prepro_start = time.time()
-        '''
-        for run_step, batch in enumerate(data_generator(), start=1):
-            token_list = batch[0][0].reshape(-1).tolist()
-            pos_list = batch[0][1].reshape(-1).tolist()
-            sent_list = batch[0][2].reshape(-1).tolist()
-            mask_list = batch[0][3].reshape(-1).tolist()
-        '''
         feed_batch = []
         for si in range(self.batch_size):
-            '''
-            feed = {
-                "input_ids": token_list[si * self.max_seq_len:(si + 1) *
-                                        self.max_seq_len],
-                "position_ids":
-                pos_list[si * self.max_seq_len:(si + 1) * self.max_seq_len],
-                "segment_ids": sent_list[si * self.max_seq_len:(si + 1) *
-                                         self.max_seq_len],
-                "input_mask":
-                mask_list[si * self.max_seq_len:(si + 1) * self.max_seq_len]
-            }
-            '''
             feed = self.reader.process(text[si])
             feed_batch.append(feed)
         prepro_end = time.time()