diff --git a/model_zoo/official/cv/FCN8s/src/data/build_seg_data.py b/model_zoo/official/cv/FCN8s/src/data/build_seg_data.py
index 8822faefd5..b5655d7166 100644
--- a/model_zoo/official/cv/FCN8s/src/data/build_seg_data.py
+++ b/model_zoo/official/cv/FCN8s/src/data/build_seg_data.py
@@ -38,7 +38,7 @@ def parse_args():
 if __name__ == '__main__':
     args = parse_args()
 
-    datas = []
+    data_list = []
     with open(args.data_lst) as f:
         lines = f.readlines()
     if args.shuffle:
@@ -65,14 +65,14 @@ if __name__ == '__main__':
             sample_['data'] = f.read()
         with open(os.path.join(args.data_root, label_path), 'rb') as f:
             sample_['label'] = f.read()
-        datas.append(sample_)
+        data_list.append(sample_)
         cnt += 1
         if cnt % 1000 == 0:
-            writer.write_raw_data(datas)
+            writer.write_raw_data(data_list)
             print('number of samples written:', cnt)
-            datas = []
+            data_list = []
 
-    if datas:
-        writer.write_raw_data(datas)
+    if data_list:
+        writer.write_raw_data(data_list)
     writer.commit()
     print('number of samples written:', cnt)
diff --git a/model_zoo/official/nlp/fasttext/src/dataset.py b/model_zoo/official/nlp/fasttext/src/dataset.py
index 7135bd4590..dc6810315c 100644
--- a/model_zoo/official/nlp/fasttext/src/dataset.py
+++ b/model_zoo/official/nlp/fasttext/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2021 Huawei Technologies Co., Ltd
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -63,6 +63,33 @@ class FastTextDataPreProcess():
         self.vec2words[1] = 'UNK'
         self.str_html = re.compile(r'<[^>]+>')
 
+    def common_block(self, _pair_sen, spacy_nlp):
+        """common block for data preprocessing"""
+        label_idx = int(_pair_sen[0]) - 1
+        if len(_pair_sen) == 3:
+            src_tokens = self.input_preprocess(src_text1=_pair_sen[1],
+                                               src_text2=_pair_sen[2],
+                                               spacy_nlp=spacy_nlp,
+                                               train_mode=True)
+            src_tokens_length = len(src_tokens)
+        elif len(_pair_sen) == 2:
+            src_tokens = self.input_preprocess(src_text1=_pair_sen[1],
+                                               src_text2=None,
+                                               spacy_nlp=spacy_nlp,
+                                               train_mode=True)
+            src_tokens_length = len(src_tokens)
+        elif len(_pair_sen) == 4:
+            if _pair_sen[2]:
+                sen_o_t = _pair_sen[1] + ' ' + _pair_sen[2]
+            else:
+                sen_o_t = _pair_sen[1]
+            src_tokens = self.input_preprocess(src_text1=sen_o_t,
+                                               src_text2=_pair_sen[3],
+                                               spacy_nlp=spacy_nlp,
+                                               train_mode=True)
+            src_tokens_length = len(src_tokens)
+        return src_tokens, src_tokens_length, label_idx
+
     def load(self):
         """data preprocess loader"""
         train_dataset_list = []
@@ -73,30 +100,8 @@ class FastTextDataPreProcess():
         with open(self.train_path, 'r', newline='', encoding='utf-8') as src_file:
             reader = csv.reader(src_file, delimiter=",", quotechar='"')
             for _, _pair_sen in enumerate(reader):
-                label_idx = int(_pair_sen[0]) - 1
-                if len(_pair_sen) == 3:
-                    src_tokens = self.input_preprocess(src_text1=_pair_sen[1],
-                                                       src_text2=_pair_sen[2],
-                                                       spacy_nlp=spacy_nlp,
-                                                       train_mode=True)
-                    src_tokens_length = len(src_tokens)
-                elif len(_pair_sen) == 2:
-                    src_tokens = self.input_preprocess(src_text1=_pair_sen[1],
-                                                       src_text2=None,
-                                                       spacy_nlp=spacy_nlp,
-                                                       train_mode=True)
-                    src_tokens_length = len(src_tokens)
-                elif len(_pair_sen) == 4:
-                    if _pair_sen[2]:
-                        sen_o_t = _pair_sen[1] + ' ' + _pair_sen[2]
-                    else:
-                        sen_o_t = _pair_sen[1]
-                    src_tokens = self.input_preprocess(src_text1=sen_o_t,
-                                                       src_text2=_pair_sen[3],
-                                                       spacy_nlp=spacy_nlp,
-                                                       train_mode=True)
-                    src_tokens_length = len(src_tokens)
-
+                src_tokens, src_tokens_length, label_idx = self.common_block(_pair_sen=_pair_sen,
+                                                                             spacy_nlp=spacy_nlp)
                 train_dataset_list.append([src_tokens, src_tokens_length, label_idx])
 
         print("Begin to process test data...")
@@ -274,7 +279,7 @@ if __name__ == '__main__':
     parser = argparse.ArgumentParser()
     parser.add_argument('--train_file', type=str, required=True, help='train dataset file path')
     parser.add_argument('--test_file', type=str, required=True, help='test dataset file path')
-    parser.add_argument('--class_num', type=int, required=True, help='Dataset classe number')
+    parser.add_argument('--class_num', type=int, required=True, help='Dataset class number')
     parser.add_argument('--ngram', type=int, default=2, required=False)
     parser.add_argument('--max_len', type=int, required=False, help='max length sentence in dataset')
     parser.add_argument('--bucket', type=ast.literal_eval, default=[64, 128, 467], help='bucket sequence length.')