Sentence -> Sequence. Removed output_mask from the special token addition methods.

examples/run_lm_finetuning.py

@@ -75,7 +75,7 @@ class TextDataset(Dataset):
             tokenized_text = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(text))
 
             while len(tokenized_text) >= block_size:  # Truncate in block of block_size
-                self.examples.append(tokenizer.add_special_tokens_single_sentence(tokenized_text[:block_size]))
+                self.examples.append(tokenizer.add_special_tokens_single_sequence(tokenized_text[:block_size]))
                 tokenized_text = tokenized_text[block_size:]
             # Note that we are loosing the last truncated example here for the sake of simplicity (no padding)
             # If your dataset is small, first you should loook for a bigger one :-) and second you

pytorch_transformers/tests/tokenization_bert_test.py

@@ -131,8 +131,8 @@ class BertTokenizationTest(CommonTestCases.CommonTokenizerTester):
         text = tokenizer.encode("sequence builders")
         text_2 = tokenizer.encode("multi-sequence build")
 
-        encoded_sentence = tokenizer.add_special_tokens_single_sentence(text)
-        encoded_pair = tokenizer.add_special_tokens_sentences_pair(text, text_2)
+        encoded_sentence = tokenizer.add_special_tokens_single_sequence(text)
+        encoded_pair = tokenizer.add_special_tokens_sequence_pair(text, text_2)
 
         assert encoded_sentence == [101] + text + [102]
         assert encoded_pair == [101] + text + [102] + text_2 + [102]

pytorch_transformers/tests/tokenization_distilbert_test.py

@@ -36,8 +36,8 @@ class DistilBertTokenizationTest(BertTokenizationTest):
         text = tokenizer.encode("sequence builders")
         text_2 = tokenizer.encode("multi-sequence build")
 
-        encoded_sentence = tokenizer.add_special_tokens_single_sentence(text)
-        encoded_pair = tokenizer.add_special_tokens_sentences_pair(text, text_2)
+        encoded_sentence = tokenizer.add_special_tokens_single_sequence(text)
+        encoded_pair = tokenizer.add_special_tokens_sequence_pair(text, text_2)
 
         assert encoded_sentence == text
         assert encoded_pair == text + [102] + text_2

pytorch_transformers/tests/tokenization_roberta_test.py

@@ -87,8 +87,8 @@ class RobertaTokenizationTest(CommonTestCases.CommonTokenizerTester):
         encoded_text_from_decode = tokenizer.encode("sequence builders", add_special_tokens=True)
         encoded_pair_from_decode = tokenizer.encode("sequence builders", "multi-sequence build", add_special_tokens=True)
 
-        encoded_sentence = tokenizer.add_special_tokens_single_sentence(text)
-        encoded_pair = tokenizer.add_special_tokens_sentences_pair(text, text_2)
+        encoded_sentence = tokenizer.add_special_tokens_single_sequence(text)
+        encoded_pair = tokenizer.add_special_tokens_sequence_pair(text, text_2)
 
         assert encoded_sentence == encoded_text_from_decode
         assert encoded_pair == encoded_pair_from_decode

pytorch_transformers/tests/tokenization_tests_commons.py

@@ -187,18 +187,18 @@ class CommonTestCases:
             for weights_list_2 in weights_lists_2:
                 self.assertListEqual(weights_list, weights_list_2)
 
-        def test_mask_output(self):
-            if sys.version_info <= (3, 0):
-                return
-
-            tokenizer = self.get_tokenizer()
-
-            if tokenizer.add_special_tokens_sentences_pair.__qualname__.split('.')[0] != "PreTrainedTokenizer":
-                seq_0 = "Test this method."
-                seq_1 = "With these inputs."
-                information = tokenizer.encode_plus(seq_0, seq_1, add_special_tokens=True, output_mask=True)
-                sequences, mask = information["sequence"], information["mask"]
-                assert len(sequences) == len(mask)
+        # def test_mask_output(self):
+        #     if sys.version_info <= (3, 0):
+        #         return
+        #
+        #     tokenizer = self.get_tokenizer()
+        #
+        #     if tokenizer.add_special_tokens_sequence_pair.__qualname__.split('.')[0] != "PreTrainedTokenizer":
+        #         seq_0 = "Test this method."
+        #         seq_1 = "With these inputs."
+        #         information = tokenizer.encode_plus(seq_0, seq_1, add_special_tokens=True, output_mask=True)
+        #         sequences, mask = information["sequence"], information["mask"]
+        #         assert len(sequences) == len(mask)
 
         def test_number_of_added_tokens(self):
             tokenizer = self.get_tokenizer()
@@ -228,7 +228,7 @@ class CommonTestCases:
 
             assert len(overflowing_tokens) == 2
             assert len(truncated_sequence) == total_length - 2
-            assert truncated_sequence == tokenizer.add_special_tokens_single_sentence(sequence[:-2])
+            assert truncated_sequence == tokenizer.add_special_tokens_single_sequence(sequence[:-2])
 
         def test_maximum_encoding_length_pair_input(self):
             tokenizer = self.get_tokenizer()
@@ -237,7 +237,7 @@ class CommonTestCases:
             seq_1 = "This is another sentence to be encoded."
 
             sequence = tokenizer.encode(seq_0, seq_1, add_special_tokens=True)
-            truncated_second_sequence = tokenizer.add_special_tokens_sentences_pair(
+            truncated_second_sequence = tokenizer.add_special_tokens_sequence_pair(
                 tokenizer.encode(seq_0),
                 tokenizer.encode(seq_1)[:-2]
             )

pytorch_transformers/tests/tokenization_xlm_test.py

@@ -72,8 +72,8 @@ class XLMTokenizationTest(CommonTestCases.CommonTokenizerTester):
         text = tokenizer.encode("sequence builders")
         text_2 = tokenizer.encode("multi-sequence build")
 
-        encoded_sentence = tokenizer.add_special_tokens_single_sentence(text)
-        encoded_pair = tokenizer.add_special_tokens_sentences_pair(text, text_2)
+        encoded_sentence = tokenizer.add_special_tokens_single_sequence(text)
+        encoded_pair = tokenizer.add_special_tokens_sequence_pair(text, text_2)
 
         assert encoded_sentence == [1] + text + [1]
         assert encoded_pair == [1] + text + [1] + text_2 + [1]

pytorch_transformers/tests/tokenization_xlnet_test.py

@@ -95,8 +95,8 @@ class XLNetTokenizationTest(CommonTestCases.CommonTokenizerTester):
         text = tokenizer.encode("sequence builders")
         text_2 = tokenizer.encode("multi-sequence build")
 
-        encoded_sentence = tokenizer.add_special_tokens_single_sentence(text)
-        encoded_pair = tokenizer.add_special_tokens_sentences_pair(text, text_2)
+        encoded_sentence = tokenizer.add_special_tokens_single_sequence(text)
+        encoded_pair = tokenizer.add_special_tokens_sequence_pair(text, text_2)
 
         assert encoded_sentence == text + [4, 3]
         assert encoded_pair == text + [4] + text_2 + [4, 3]

pytorch_transformers/tokenization_bert.py

@@ -187,26 +187,21 @@ class BertTokenizer(PreTrainedTokenizer):
         out_string = ' '.join(tokens).replace(' ##', '').strip()
         return out_string
 
-    def add_special_tokens_single_sentence(self, token_ids):
+    def add_special_tokens_single_sequence(self, token_ids):
         """
         Adds special tokens to the a sequence for sequence classification tasks.
         A BERT sequence has the following format: [CLS] X [SEP]
         """
         return [self.cls_token_id] + token_ids + [self.sep_token_id]
 
-    def add_special_tokens_sentences_pair(self, token_ids_0, token_ids_1, output_mask=False):
+    def add_special_tokens_sequence_pair(self, token_ids_0, token_ids_1):
         """
         Adds special tokens to a sequence pair for sequence classification tasks.
         A BERT sequence pair has the following format: [CLS] A [SEP] B [SEP]
         """
         sep = [self.sep_token_id]
         cls = [self.cls_token_id]
-        if output_mask:
-            return (
-                cls + token_ids_0 + sep + token_ids_1 + sep,
-                [0] * len(cls + token_ids_0 + sep) + [1] * len(token_ids_1 + sep)
-            )
-        else:
+
         return cls + token_ids_0 + sep + token_ids_1 + sep
 
     def save_vocabulary(self, vocab_path):

pytorch_transformers/tokenization_distilbert.py

@@ -61,10 +61,10 @@ class DistilBertTokenizer(BertTokenizer):
     pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 
-    def add_special_tokens_single_sentence(self, token_ids):
+    def add_special_tokens_single_sequence(self, token_ids):
         return token_ids
 
-    def add_special_tokens_sentences_pair(self, token_ids_0, token_ids_1, output_mask=False):
+    def add_special_tokens_sequence_pair(self, token_ids_0, token_ids_1, output_mask=False):
         sep = [self.sep_token_id]
         if output_mask:
             return (

pytorch_transformers/tokenization_roberta.py

@@ -81,24 +81,18 @@ class RobertaTokenizer(GPT2Tokenizer):
                                                sep_token=sep_token, cls_token=cls_token, pad_token=pad_token,
                                                mask_token=mask_token, **kwargs)
 
-    def add_special_tokens_single_sentence(self, token_ids):
+    def add_special_tokens_single_sequence(self, token_ids):
         """
         Adds special tokens to a sequence for sequence classification tasks.
         A RoBERTa sequence has the following format: <s> X </s>
         """
         return [self.cls_token_id] + token_ids + [self.sep_token_id]
 
-    def add_special_tokens_sentences_pair(self, token_ids_0, token_ids_1, output_mask=False):
+    def add_special_tokens_sequence_pair(self, token_ids_0, token_ids_1):
         """
         Adds special tokens to a sequence pair for sequence classification tasks.
         A RoBERTa sequence pair has the following format: <s> A </s></s> B </s>
         """
         sep = [self.sep_token_id]
         cls = [self.cls_token_id]
-        if output_mask:
-            return (
-                cls + token_ids_0 + sep + sep + token_ids_1 + sep,
-                [0] * len(cls + token_ids_0 + sep + sep) + [1] * len(token_ids_1 + sep)
-            )
-        else:
         return cls + token_ids_0 + sep + sep + token_ids_1 + sep

pytorch_transformers/tokenization_utils.py

@@ -708,7 +708,7 @@ class PreTrainedTokenizer(object):
         if text_pair is None:
             if add_special_tokens:
                 sequence_tokens = self.convert_tokens_to_ids(self.tokenize(text, **kwargs))
-                return self.add_special_tokens_single_sentence(sequence_tokens)
+                return self.add_special_tokens_single_sequence(sequence_tokens)
             else:
                 ids = self.convert_tokens_to_ids(self.tokenize(text, **kwargs))
                 return ids
@@ -717,7 +717,7 @@ class PreTrainedTokenizer(object):
         second_sentence_tokens = [self._convert_token_to_id(token) for token in self.tokenize(text_pair, **kwargs)]
 
         if add_special_tokens:
-            return self.add_special_tokens_sentences_pair(first_sentence_tokens, second_sentence_tokens)
+            return self.add_special_tokens_sequence_pair(first_sentence_tokens, second_sentence_tokens)
         else:
             logger.warning("No special tokens were added. The two sequences have been concatenated.")
             return first_sentence_tokens + second_sentence_tokens
@@ -747,7 +747,7 @@ class PreTrainedTokenizer(object):
                 if max_length:
                     information["overflowing_tokens"] = sequence_tokens[max_length - n_added_tokens:]
                     sequence_tokens = sequence_tokens[:max_length - n_added_tokens]
-                sequence = self.add_special_tokens_single_sentence(sequence_tokens)
+                sequence = self.add_special_tokens_single_sequence(sequence_tokens)
             else:
                 sequence_tokens = self.convert_tokens_to_ids(self.tokenize(text, **kwargs))
                 if max_length:
@@ -774,16 +774,13 @@ class PreTrainedTokenizer(object):
                             information["overflowing_tokens"] = second_sentence_tokens[max_length - f_len - n_added_tokens:]
                             second_sentence_tokens = second_sentence_tokens[:max_length - f_len - n_added_tokens]
 
-                encoded_sequence = self.add_special_tokens_sentences_pair(
+                sequence = self.add_special_tokens_sequence_pair(
                     first_sentence_tokens,
-                    second_sentence_tokens,
-                    output_mask
+                    second_sentence_tokens
                 )
 
-                if output_mask:
-                    sequence, information["mask"] = encoded_sequence
-                else:
-                    sequence = encoded_sequence
+                # if output_mask:
+                #     sequence, information["mask"] = encoded_sequence
 
                 information["sequence"] = sequence
             else:
@@ -800,11 +797,11 @@ class PreTrainedTokenizer(object):
 
         return information
 
-    def add_special_tokens_single_sentence(self, token_ids):
+    def add_special_tokens_single_sequence(self, token_ids):
         logger.warning("This tokenizer does not make use of special tokens. The sequence has been returned with no modification.")
         return token_ids
 
-    def add_special_tokens_sentences_pair(self, token_ids_0, token_ids_1, output_mask=False):
+    def add_special_tokens_sequence_pair(self, token_ids_0, token_ids_1):
         logger.warning("This tokenizer does not make use of special tokens. The two sequences have been concatenated.")
         return token_ids_0 + token_ids_1
 

pytorch_transformers/tokenization_xlm.py

@@ -754,27 +754,20 @@ class XLMTokenizer(PreTrainedTokenizer):
         out_string = ''.join(tokens).replace('</w>', ' ').strip()
         return out_string
 
-    def add_special_tokens_single_sentence(self, token_ids):
+    def add_special_tokens_single_sequence(self, token_ids):
         """
         Adds special tokens to a sequence for sequence classification tasks.
         An XLM sequence has the following format: [CLS] X [SEP]
         """
         return [self.cls_token_id] + token_ids + [self.sep_token_id]
 
-    def add_special_tokens_sentences_pair(self, token_ids_0, token_ids_1, output_mask=False):
+    def add_special_tokens_sequence_pair(self, token_ids_0, token_ids_1):
         """
         Adds special tokens to a sequence pair for sequence classification tasks.
         An XLM sequence pair has the following format: [CLS] A [SEP] B [SEP]
         """
         sep = [self.sep_token_id]
         cls = [self.cls_token_id]
-
-        if output_mask:
-            return (
-                cls + token_ids_0 + sep + token_ids_1 + sep,
-                [0] * len(cls + token_ids_0 + sep) + [1] * len(token_ids_1 + sep)
-            )
-        else:
         return cls + token_ids_0 + sep + token_ids_1 + sep
 
     def save_vocabulary(self, save_directory):

pytorch_transformers/tokenization_xlnet.py

@@ -181,7 +181,7 @@ class XLNetTokenizer(PreTrainedTokenizer):
         out_string = ''.join(tokens).replace(SPIECE_UNDERLINE, ' ').strip()
         return out_string
 
-    def add_special_tokens_single_sentence(self, token_ids):
+    def add_special_tokens_single_sequence(self, token_ids):
         """
         Adds special tokens to a sequence pair for sequence classification tasks.
         An XLNet sequence pair has the following format: A [SEP] B [SEP][CLS]
@@ -190,7 +190,7 @@ class XLNetTokenizer(PreTrainedTokenizer):
         cls = [self.cls_token_id]
         return token_ids + sep + cls
 
-    def add_special_tokens_sentences_pair(self, token_ids_0, token_ids_1, output_mask=False):
+    def add_special_tokens_sequence_pair(self, token_ids_0, token_ids_1):
         """
         Adds special tokens to a sequence for sequence classification tasks.
         An XLNet sequence has the following format: X [SEP][CLS]
@@ -199,12 +199,6 @@ class XLNetTokenizer(PreTrainedTokenizer):
         sep = [self.sep_token_id]
         cls = [self.cls_token_id]
         cls_segment_ids = [2]
-        if output_mask:
-            return (
-                token_ids_0 + sep + token_ids_1 + sep + cls,
-                [0] * len(token_ids_0 + sep) + [1] * len(token_ids_1 + sep) + cls_segment_ids
-            )
-        else:
         return token_ids_0 + sep + token_ids_1 + sep + cls
 
     def save_vocabulary(self, save_directory):