Storing the feature of each choice as a dict for readability.

examples/run_swag.py

@@ -73,9 +73,17 @@ class InputFeatures(object):
                  example_id,
                  choices_features,
                  label
+
     ):
         self.example_id = example_id
-        self.choices_features = choices_features
+        self.choices_features = [
+            {
+                'input_ids': input_ids,
+                'input_mask': input_mask,
+                'segment_ids': segment_ids
+            }
+            for _, input_ids, input_mask, segment_ids in choices_features
+        ]
         self.label = label
 
 def read_swag_examples(input_file, is_training):
@@ -181,6 +189,7 @@ def convert_examples_to_features(examples, tokenizer, max_seq_length,
 
     return features
 
+
 def _truncate_seq_pair(tokens_a, tokens_b, max_length):
     """Truncates a sequence pair in place to the maximum length."""
 
@@ -207,4 +216,11 @@ if __name__ == "__main__":
         print("###########################")
         print(example)
     tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
-    features = convert_examples_to_features(examples, tokenizer, max_seq_length, is_training)
+    features = convert_examples_to_features(examples[:500], tokenizer, max_seq_length, is_training)
+    for i in range(10):
+        choice_feature_list = features[i].choices_features
+        for choice_idx, choice_feature in enumerate(choice_feature_list):
+            print(f'choice_idx: {choice_idx}')
+            print(f'input_ids: {" ".join(map(str, choice_feature["input_ids"]))}')
+            print(f'input_mask: {" ".join(map(str, choice_feature["input_mask"]))}')
+            print(f'segment_ids: {" ".join(map(str, choice_feature["segment_ids"]))}')