`output_token_type` -> `token_type_ids`

examples/utils_glue.py

@@ -413,7 +413,7 @@ def convert_examples_to_features(examples, label_list, max_seq_length,
             max_length=max_seq_length,
             truncate_first_sequence=True  # We're truncating the first sequence as a priority
         )
-        input_ids, segment_ids = inputs["input_ids"], inputs["output_token_type"]
+        input_ids, segment_ids = inputs["input_ids"], inputs["token_type_ids"]
 
         # The mask has 1 for real tokens and 0 for padding tokens. Only real
         # tokens are attended to.

pytorch_transformers/tests/tokenization_tests_commons.py

@@ -197,7 +197,7 @@ class CommonTestCases:
                 seq_0 = "Test this method."
                 seq_1 = "With these inputs."
                 information = tokenizer.encode_plus(seq_0, seq_1, add_special_tokens=True, output_token_type=True)
-                sequences, mask = information["input_ids"], information["output_token_type"]
+                sequences, mask = information["input_ids"], information["token_type_ids"]
                 assert len(sequences) == len(mask)
 
         def test_number_of_added_tokens(self):

pytorch_transformers/tokenization_utils.py

@@ -765,7 +765,7 @@ class PreTrainedTokenizer(object):
                 information["input_ids"] = sequence_tokens
 
             if output_token_type:
-                information["output_token_type"] = [0] * len(information["input_ids"])
+                information["token_type_ids"] = [0] * len(information["input_ids"])
         else:
             first_sentence_tokens = get_input_ids(text)
             second_sentence_tokens = get_input_ids(text_pair)
@@ -780,7 +780,7 @@ class PreTrainedTokenizer(object):
                 )
 
                 if output_token_type:
-                    information["output_token_type"] = self.create_mask_from_sequences(text, text_pair)
+                    information["token_type_ids"] = self.create_mask_from_sequences(text, text_pair)
             else:
                 logger.warning("No special tokens were added. The two sequences have been concatenated.")
                 sequence = first_sentence_tokens + second_sentence_tokens
@@ -789,7 +789,7 @@ class PreTrainedTokenizer(object):
                     information["overflowing_tokens"] = sequence[max_length - stride:]
                     sequence = sequence[:max_length]
                 if output_token_type:
-                    information["output_token_type"] = [0] * len(sequence)
+                    information["token_type_ids"] = [0] * len(sequence)
 
                 information["input_ids"] = sequence