diff --git a/PaddleNLP/PaddleDialogue/dialogue_general_understanding/dgu/evaluation.py b/PaddleNLP/PaddleDialogue/dialogue_general_understanding/dgu/evaluation.py
index 43d3fe636eadea3b1b8da6c7c2e082ea7e1e246b..d8c06944e20004547ae5516b9ce70eb338ecc68b 100644
--- a/PaddleNLP/PaddleDialogue/dialogue_general_understanding/dgu/evaluation.py
+++ b/PaddleNLP/PaddleDialogue/dialogue_general_understanding/dgu/evaluation.py
@@ -22,26 +22,27 @@ class EvalDA(object):
     """
     evaluate da testset, swda|mrda
     """
-    def __init__(self, task_name, pred, refer): 
+
+    def __init__(self, task_name, pred, refer):
         """
         predict file
         """
         self.pred_file = pred
         self.refer_file = refer
 
-    def load_data(self): 
+    def load_data(self):
         """
         load reference label and predict label
         """
         pred_label = []
         refer_label = []
         fr = io.open(self.refer_file, 'r', encoding="utf8")
-        for line in fr:  
+        for line in fr:
             label = line.rstrip('\n').split('\t')[1]
             refer_label.append(int(label))
         idx = 0
         fr = io.open(self.pred_file, 'r', encoding="utf8")
-        for line in fr: 
+        for line in fr:
             elems = line.rstrip('\n').split('\t')
             if len(elems) != 2 or not elems[0].isdigit():
                 continue
@@ -49,15 +50,15 @@ class EvalDA(object):
             pred_label.append(tag_id)
         return pred_label, refer_label
 
-    def evaluate(self): 
+    def evaluate(self):
         """
         calculate acc metrics
         """
         pred_label, refer_label = self.load_data()
         common_num = 0
         total_num = len(pred_label)
-        for i in range(total_num): 
-            if pred_label[i] == refer_label[i]: 
+        for i in range(total_num):
+            if pred_label[i] == refer_label[i]:
                 common_num += 1
         acc = float(common_num) / total_num
         return acc
@@ -67,26 +68,27 @@ class EvalATISIntent(object):
     """
     evaluate da testset, swda|mrda
     """
-    def __init__(self, pred, refer): 
+
+    def __init__(self, pred, refer):
         """
         predict file
         """
         self.pred_file = pred
         self.refer_file = refer
 
-    def load_data(self): 
+    def load_data(self):
         """
         load reference label and predict label
         """
         pred_label = []
         refer_label = []
         fr = io.open(self.refer_file, 'r', encoding="utf8")
-        for line in fr:  
+        for line in fr:
             label = line.rstrip('\n').split('\t')[0]
             refer_label.append(int(label))
         idx = 0
         fr = io.open(self.pred_file, 'r', encoding="utf8")
-        for line in fr: 
+        for line in fr:
             elems = line.rstrip('\n').split('\t')
             if len(elems) != 2 or not elems[0].isdigit():
                 continue
@@ -94,45 +96,46 @@ class EvalATISIntent(object):
             pred_label.append(tag_id)
         return pred_label, refer_label
 
-    def evaluate(self): 
+    def evaluate(self):
         """
         calculate acc metrics
         """
         pred_label, refer_label = self.load_data()
         common_num = 0
         total_num = len(pred_label)
-        for i in range(total_num): 
-            if pred_label[i] == refer_label[i]: 
+        for i in range(total_num):
+            if pred_label[i] == refer_label[i]:
                 common_num += 1
         acc = float(common_num) / total_num
         return acc
 
 
-class EvalATISSlot(object): 
+class EvalATISSlot(object):
     """
     evaluate atis slot
     """
-    def __init__(self, pred, refer): 
+
+    def __init__(self, pred, refer):
         """
         pred file
         """
         self.pred_file = pred
         self.refer_file = refer
 
-    def load_data(self): 
+    def load_data(self):
         """
         load reference label and predict label
         """
         pred_label = []
         refer_label = []
         fr = io.open(self.refer_file, 'r', encoding="utf8")
-        for line in fr: 
+        for line in fr:
             labels = line.rstrip('\n').split('\t')[1].split()
             labels = [int(l) for l in labels]
             refer_label.append(labels)
         fr = io.open(self.pred_file, 'r', encoding="utf8")
-        for line in fr: 
-            if len(line.split('\t')) != 2 or not line[0].isdigit(): 
+        for line in fr:
+            if len(line.split('\t')) != 2 or not line[0].isdigit():
                 continue
             labels = line.rstrip('\n').split('\t')[1].split()[1:]
             labels = [int(l) for l in labels]
@@ -140,15 +143,15 @@ class EvalATISSlot(object):
         pred_label_equal = []
         refer_label_equal = []
         assert len(refer_label) == len(pred_label)
-        for i in range(len(refer_label)): 
+        for i in range(len(refer_label)):
             num = len(refer_label[i])
             refer_label_equal.extend(refer_label[i])
-            pred_label[i] = pred_label[i][: num]
+            pred_label[i] = pred_label[i][:num]
             pred_label_equal.extend(pred_label[i])
 
         return pred_label_equal, refer_label_equal
 
-    def evaluate(self):  
+    def evaluate(self):
         """
         evaluate f1_micro score
         """
@@ -156,13 +159,13 @@ class EvalATISSlot(object):
         tp = dict()
         fn = dict()
         fp = dict()
-        for i in range(len(refer_label)): 
+        for i in range(len(refer_label)):
             if refer_label[i] == pred_label[i]:
-                if refer_label[i] not in tp: 
+                if refer_label[i] not in tp:
                     tp[refer_label[i]] = 0
                 tp[refer_label[i]] += 1
-            else: 
-                if pred_label[i] not in fp: 
+            else:
+                if pred_label[i] not in fp:
                     fp[pred_label[i]] = 0
                 fp[pred_label[i]] += 1
                 if refer_label[i] not in fn:
@@ -170,17 +173,17 @@ class EvalATISSlot(object):
                 fn[refer_label[i]] += 1
 
         results = ["label    precision    recall"]
-        for i in range(0, 130): 
-            if i not in tp: 
+        for i in range(0, 130):
+            if i not in tp:
                 results.append(" %s:    0.0     0.0" % i)
                 continue
-            if i in fp: 
+            if i in fp:
                 precision = float(tp[i]) / (tp[i] + fp[i])
-            else: 
+            else:
                 precision = 1.0
-            if i in fn: 
+            if i in fn:
                 recall = float(tp[i]) / (tp[i] + fn[i])
-            else: 
+            else:
                 recall = 1.0
             results.append(" %s:    %.4f    %.4f" % (i, precision, recall))
         tp_total = sum(tp.values())
@@ -193,32 +196,33 @@ class EvalATISSlot(object):
         return "\n".join(results)
 
 
-class EvalUDC(object): 
+class EvalUDC(object):
     """
     evaluate udc
     """
-    def __init__(self, pred, refer): 
+
+    def __init__(self, pred, refer):
         """
         predict file
         """
         self.pred_file = pred
         self.refer_file = refer
 
-    def load_data(self): 
+    def load_data(self):
         """
         load reference label and predict label
         """
-        data = [] 
+        data = []
         refer_label = []
         fr = io.open(self.refer_file, 'r', encoding="utf8")
-        for line in fr: 
+        for line in fr:
             label = line.rstrip('\n').split('\t')[0]
             refer_label.append(label)
         idx = 0
         fr = io.open(self.pred_file, 'r', encoding="utf8")
-        for line in fr: 
+        for line in fr:
             elems = line.rstrip('\n').split('\t')
-            if len(elems) != 2 or not elems[0].isdigit(): 
+            if len(elems) != 2 or not elems[0].isdigit():
                 continue
             match_prob = elems[1]
             data.append((float(match_prob), int(refer_label[idx])))
@@ -230,8 +234,8 @@ class EvalUDC(object):
         calculate precision in recall n
         """
         pos_score = data[ind][0]
-        curr = data[ind: ind + m]
-        curr = sorted(curr, key = lambda x: x[0], reverse = True)
+        curr = data[ind:ind + m]
+        curr = sorted(curr, key=lambda x: x[0], reverse=True)
 
         if curr[n - 1][0] <= pos_score:
             return 1
@@ -241,20 +245,20 @@ class EvalUDC(object):
         """
         calculate udc data
         """
-        data = self.load_data() 
+        data = self.load_data()
         assert len(data) % 10 == 0
-        
+
         p_at_1_in_2 = 0.0
         p_at_1_in_10 = 0.0
         p_at_2_in_10 = 0.0
         p_at_5_in_10 = 0.0
 
-        length = len(data)/10
+        length = int(len(data) / 10)
 
         for i in range(0, length):
             ind = i * 10
             assert data[ind][1] == 1
-    
+
             p_at_1_in_2 += self.get_p_at_n_in_m(data, 1, 2, ind)
             p_at_1_in_10 += self.get_p_at_n_in_m(data, 1, 10, ind)
             p_at_2_in_10 += self.get_p_at_n_in_m(data, 2, 10, ind)
@@ -262,13 +266,14 @@ class EvalUDC(object):
 
         metrics_out = [p_at_1_in_2 / length, p_at_1_in_10 / length, \
                 p_at_2_in_10 / length, p_at_5_in_10 / length]
-        return metrics_out 
+        return metrics_out
 
 
-class EvalDSTC2(object): 
+class EvalDSTC2(object):
     """
     evaluate dst testset, dstc2
     """
+
     def __init__(self, task_name, pred, refer):
         """
         predict file
@@ -277,39 +282,39 @@ class EvalDSTC2(object):
         self.pred_file = pred
         self.refer_file = refer
 
-    def load_data(self): 
+    def load_data(self):
         """
         load reference label and predict label
         """
         pred_label = []
         refer_label = []
         fr = io.open(self.refer_file, 'r', encoding="utf8")
-        for line in fr: 
+        for line in fr:
             line = line.strip('\n')
             labels = [int(l) for l in line.split('\t')[-1].split()]
             labels = sorted(list(set(labels)))
             refer_label.append(" ".join([str(l) for l in labels]))
         all_pred = []
         fr = io.open(self.pred_file, 'r', encoding="utf8")
-        for line in fr: 
+        for line in fr:
             line = line.strip('\n')
             all_pred.append(line)
         all_pred = all_pred[len(all_pred) - len(refer_label):]
-        for line in all_pred: 
+        for line in all_pred:
             labels = [int(l) for l in line.split('\t')[-1].split()]
             labels = sorted(list(set(labels)))
             pred_label.append(" ".join([str(l) for l in labels]))
         return pred_label, refer_label
 
-    def evaluate(self): 
+    def evaluate(self):
         """
         calculate joint acc && overall acc
         """
         overall_all = 0.0
         correct_joint = 0
         pred_label, refer_label = self.load_data()
-        for i in range(len(refer_label)): 
-            if refer_label[i] != pred_label[i]: 
+        for i in range(len(refer_label)):
+            if refer_label[i] != pred_label[i]:
                 continue
             correct_joint += 1
         joint_all = float(correct_joint) / len(refer_label)
@@ -317,9 +322,9 @@ class EvalDSTC2(object):
         return metrics_out
 
 
-def evaluate(task_name, pred_file, refer_file): 
+def evaluate(task_name, pred_file, refer_file):
     """evaluate task metrics"""
-    if task_name.lower() == 'udc': 
+    if task_name.lower() == 'udc':
         eval_inst = EvalUDC(pred_file, refer_file)
         eval_metrics = eval_inst.evaluate()
         print("MATCHING TASK: %s metrics in testset: " % task_name)
@@ -328,45 +333,46 @@ def evaluate(task_name, pred_file, refer_file):
         print("R2@10: %s" % eval_metrics[2])
         print("R5@10: %s" % eval_metrics[3])
 
-    elif task_name.lower() in ['swda', 'mrda']: 
+    elif task_name.lower() in ['swda', 'mrda']:
         eval_inst = EvalDA(task_name.lower(), pred_file, refer_file)
         eval_metrics = eval_inst.evaluate()
         print("DA TASK: %s metrics in testset: " % task_name)
         print("ACC: %s" % eval_metrics)
 
-    elif task_name.lower() == 'atis_intent': 
+    elif task_name.lower() == 'atis_intent':
         eval_inst = EvalATISIntent(pred_file, refer_file)
         eval_metrics = eval_inst.evaluate()
         print("INTENTION TASK: %s metrics in testset: " % task_name)
         print("ACC: %s" % eval_metrics)
 
-    elif task_name.lower() == 'atis_slot': 
+    elif task_name.lower() == 'atis_slot':
         eval_inst = EvalATISSlot(pred_file, refer_file)
         eval_metrics = eval_inst.evaluate()
         print("SLOT FILLING TASK: %s metrics in testset: " % task_name)
         print(eval_metrics)
-    elif task_name.lower() in ['dstc2', 'dstc2_asr']: 
+    elif task_name.lower() in ['dstc2', 'dstc2_asr']:
         eval_inst = EvalDSTC2(task_name.lower(), pred_file, refer_file)
         eval_metrics = eval_inst.evaluate()
         print("DST TASK: %s metrics in testset: " % task_name)
         print("JOINT ACC: %s" % eval_metrics[0])
-    elif task_name.lower() == "multi-woz": 
+    elif task_name.lower() == "multi-woz":
         eval_inst = EvalMultiWoz(pred_file, refer_file)
         eval_metrics = eval_inst.evaluate()
         print("DST TASK: %s metrics in testset: " % task_name)
         print("JOINT ACC: %s" % eval_metrics[0])
         print("OVERALL ACC: %s" % eval_metrics[1])
-    else: 
-        print("task name not in [udc|swda|mrda|atis_intent|atis_slot|dstc2|dstc2_asr|multi-woz]")
+    else:
+        print(
+            "task name not in [udc|swda|mrda|atis_intent|atis_slot|dstc2|dstc2_asr|multi-woz]"
+        )
 
 
-if __name__ == "__main__": 
-    if len(sys.argv[1:]) < 3: 
+if __name__ == "__main__":
+    if len(sys.argv[1:]) < 3:
         print("please input task_name predict_file reference_file")
 
     task_name = sys.argv[1]
     pred_file = sys.argv[2]
     refer_file = sys.argv[3]
 
-
     evaluate(task_name, pred_file, refer_file)