Fixing problems in convert_examples_to_features.

examples/run_swag.py

@@ -70,20 +70,13 @@ class SwagExample(object):
 
 class InputFeatures(object):
     def __init__(self,
-                 unique_id,
                  example_id,
-                 input_ids,
+                 choices_features,
-                 input_mask,
+                 label
-                 segment_ids,
-                 label_id
     ):
-        self.unique_id = unique_id
         self.example_id = example_id
-        self.input_ids = input_ids
+        self.choices_features = choices_features
-        self.input_mask = input_mask
+        self.label = label
-        self.segment_ids = segment_ids
-        self.label_id = label_id
-
 
 def read_swag_examples(input_file, is_training):
     input_df = pd.read_csv(input_file)
@@ -145,7 +138,7 @@ def convert_examples_to_features(examples, tokenizer, max_seq_length,
             # place so that the total length is less than the
             # specified length.  Account for [CLS], [SEP], [SEP] with
             # "- 3"
-            _truncate_seq_pair(context_tokens, ending_tokens, max_seq_length - 3)
+            _truncate_seq_pair(context_tokens_choice, ending_tokens, max_seq_length - 3)
 
             tokens = ["[CLS]"] + context_tokens_choice + ["[SEP]"] + ending_tokens + ["[SEP]"]
             segment_ids = [0] * (len(context_tokens_choice) + 2) + [1] * (len(ending_tokens) + 1)
@@ -178,7 +171,15 @@ def convert_examples_to_features(examples, tokenizer, max_seq_length,
             if is_training:
                 logger.info(f"label: {label}")
 
+        features.append(
+            InputFeatures(
+                example_id = example.swag_id,
+                choices_features = choices_features,
+                label = label
+            )
+        )
 
+    return features
 
 def _truncate_seq_pair(tokens_a, tokens_b, max_length):
     """Truncates a sequence pair in place to the maximum length."""
@@ -206,4 +207,4 @@ if __name__ == "__main__":
         print("###########################")
         print(example)
     tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
-    convert_examples_to_features(examples, tokenizer, max_seq_length, is_training)
+    features = convert_examples_to_features(examples, tokenizer, max_seq_length, is_training)