diff --git a/fluid/sequence_tagging_for_ner/infer.py b/fluid/sequence_tagging_for_ner/infer.py
index 2d0bd9496ed2ec1db019a0124905093e0b12531a..acf98d0f15f7f493654822751fb2619de20e5505 100644
--- a/fluid/sequence_tagging_for_ner/infer.py
+++ b/fluid/sequence_tagging_for_ner/infer.py
@@ -1,7 +1,10 @@
+from __future__ import print_function
+
 import numpy as np
+import six
 
+import paddle
 import paddle.fluid as fluid
-import paddle.v2 as paddle
 
 from network_conf import ner_net
 import reader
@@ -33,9 +36,9 @@ def infer(model_path, batch_size, test_data_file, vocab_file, target_file,
         [inference_program, feed_target_names,
          fetch_targets] = fluid.io.load_inference_model(model_path, exe)
         for data in test_data():
-            word = to_lodtensor(map(lambda x: x[0], data), place)
-            mark = to_lodtensor(map(lambda x: x[1], data), place)
-            target = to_lodtensor(map(lambda x: x[2], data), place)
+            word = to_lodtensor([x[0] for x in data], place)
+            mark = to_lodtensor([x[1] for x in data], place)
+            target = to_lodtensor([x[2] for x in data], place)
             crf_decode = exe.run(
                 inference_program,
                 feed={"word": word,
@@ -46,19 +49,19 @@ def infer(model_path, batch_size, test_data_file, vocab_file, target_file,
             lod_info = (crf_decode[0].lod())[0]
             np_data = np.array(crf_decode[0])
             assert len(data) == len(lod_info) - 1
-            for sen_index in xrange(len(data)):
+            for sen_index in six.moves.xrange(len(data)):
                 assert len(data[sen_index][0]) == lod_info[
                     sen_index + 1] - lod_info[sen_index]
                 word_index = 0
-                for tag_index in xrange(lod_info[sen_index],
+                for tag_index in six.moves.xrange(lod_info[sen_index],
                                         lod_info[sen_index + 1]):
                     word = word_reverse_dict[data[sen_index][0][word_index]]
                     gold_tag = label_reverse_dict[data[sen_index][2][
                         word_index]]
                     tag = label_reverse_dict[np_data[tag_index][0]]
-                    print word + "\t" + gold_tag + "\t" + tag
+                    print(word + "\t" + gold_tag + "\t" + tag)
                     word_index += 1
-                print ""
+                print("")
 
 
 if __name__ == "__main__":
diff --git a/fluid/sequence_tagging_for_ner/train.py b/fluid/sequence_tagging_for_ner/train.py
index 7a6e992df459157d0a74839d1e3fbef7213a4b14..7ad5f28546c1971aa247f14079af8fa44d5a02ce 100644
--- a/fluid/sequence_tagging_for_ner/train.py
+++ b/fluid/sequence_tagging_for_ner/train.py
@@ -1,7 +1,10 @@
+from __future__ import print_function
+
 import os
 import math
 import time
 import numpy as np
+import six
 
 import paddle
 import paddle.fluid as fluid
@@ -15,9 +18,9 @@ from utils_extend import to_lodtensor, get_embedding
 def test(exe, chunk_evaluator, inference_program, test_data, place):
     chunk_evaluator.reset(exe)
     for data in test_data():
-        word = to_lodtensor(map(lambda x: x[0], data), place)
-        mark = to_lodtensor(map(lambda x: x[1], data), place)
-        target = to_lodtensor(map(lambda x: x[2], data), place)
+        word = to_lodtensor([x[0] for x in data], place)
+        mark = to_lodtensor([x[1] for x in data], place)
+        target = to_lodtensor([x[2] for x in data], place)
         acc = exe.run(inference_program,
                       feed={"word": word,
                             "mark": mark,
@@ -97,7 +100,7 @@ def main(train_data_file,
     embedding_param = fluid.global_scope().find_var(embedding_name).get_tensor()
     embedding_param.set(word_vector_values, place)
 
-    for pass_id in xrange(num_passes):
+    for pass_id in six.moves.xrange(num_passes):
         chunk_evaluator.reset(exe)
         for batch_id, data in enumerate(train_reader()):
             cost, batch_precision, batch_recall, batch_f1_score = exe.run(
@@ -142,6 +145,5 @@ if __name__ == "__main__":
         emb_file="data/wordVectors.txt",
         model_save_dir="models",
         num_passes=1000,
-        batch_size=1,
         use_gpu=False,
         parallel=False)