Fix MNLI error (#105)

paddlehub/dataset/dataset.py

@@ -45,7 +45,7 @@ class InputExample(object):
         if self.text_b is None:
             return "text={}\tlabel={}".format(self.text_a, self.label)
         else:
-            return "text_a={}\ttext_b{},label={}".format(
+            return "text_a={}\ttext_b={},label={}".format(
                 self.text_a, self.text_b, self.label)
 
 

paddlehub/dataset/glue.py

@@ -43,7 +43,7 @@ class GLUE(HubDataset):
         ]:
             raise Exception(
                 sub_dataset +
-                "is not in GLUE benchmark. Please confirm the data set")
+                " is not in GLUE benchmark. Please confirm the data set")
         self.sub_dataset = sub_dataset
         self.dataset_dir = os.path.join(DATA_HOME, "glue_data")
 
@@ -120,14 +120,15 @@ class GLUE(HubDataset):
             reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
             examples = []
             seq_id = 0
+            if self.sub_dataset != 'CoLA' or wo_label:
                 header = next(reader)  # skip header
             if self.sub_dataset in [
                     'MRPC',
             ]:
                 if wo_label:
-                    label_index, text_a_index, text_b_index = [None, -1, -2]
+                    label_index, text_a_index, text_b_index = [None, -2, -1]
                 else:
-                    label_index, text_a_index, text_b_index = [0, -1, -2]
+                    label_index, text_a_index, text_b_index = [0, -2, -1]
             elif self.sub_dataset in [
                     'QNLI',
             ]:
@@ -160,9 +161,9 @@ class GLUE(HubDataset):
                     'MNLI',
             ]:
                 if wo_label:
-                    label_index, text_a_index, text_b_index = [None, -2, -1]
+                    label_index, text_a_index, text_b_index = [None, 8, 9]
                 else:
-                    label_index, text_a_index, text_b_index = [-1, -4, -3]
+                    label_index, text_a_index, text_b_index = [-1, 8, 9]
             elif self.sub_dataset in ['CoLA']:
                 if wo_label:
                     label_index, text_a_index, text_b_index = [None, 1, None]
@@ -170,9 +171,9 @@ class GLUE(HubDataset):
                     label_index, text_a_index, text_b_index = [1, 3, None]
             elif self.sub_dataset in ['STS-B']:
                 if wo_label:
-                    label_index, text_a_index, text_b_index = [None, -1, -2]
+                    label_index, text_a_index, text_b_index = [None, -2, -1]
                 else:
-                    label_index, text_a_index, text_b_index = [-1, -2, -3]
+                    label_index, text_a_index, text_b_index = [-1, -3, -2]
 
             for line in reader:
                 try:
@@ -191,26 +192,20 @@ class GLUE(HubDataset):
 
 
 if __name__ == "__main__":
-    ds = GLUE(sub_dataset='CoLA')
-    total_len = 0
-    max_len = 0
-    total_num = over_num = 0
-    overlen = []
-    for e in ds.get_predict_examples():
-        length = len(e.text_a.split()) + len(
-            e.text_b.split()) if e.text_b else len(e.text_a.split())
-        total_len += length
-        if length > max_len:
-            max_len = length
-        total_num += 1
-        if length > 128:
-            over_num += 1
-            overstr = ("\ntext_a: " + e.text_a + "\ntext_b:" +
-                       e.text_b) if e.text_b else e.text_a
-            overlen.append(overstr)
-    avg = total_len / total_num
-    for o in overlen[:2]:
-        print("The data length>128:{}".format(o))
-    print(
-        "The total number: {}\nThe avrage length: {}\nthe max length: {}\nthe number of data length > 128:  {}"
-        .format(total_num, avg, max_len, over_num))
+    for sub_dataset in [
+            'CoLA', 'MNLI', 'MRPC', 'QNLI', 'QQP', 'RTE', 'SST-2', 'STS-B'
+    ]:
+        print(sub_dataset)
+        ds = GLUE(sub_dataset=sub_dataset)
+        for e in ds.get_train_examples()[:2]:
+            print(e)
+        print()
+        for e in ds.get_dev_examples()[:2]:
+            print(e)
+        print()
+        for e in ds.get_test_examples()[:2]:
+            print(e)
+        print()
+        for e in ds.get_predict_examples()[:2]:
+            print(e)
+        print()