Mask computing in standalone method. Tests.

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):
+        def test_mask_output(self):
-        #     if sys.version_info <= (3, 0):
+            if sys.version_info <= (3, 0):
-        #         return
+                return
-        #
+
-        #     tokenizer = self.get_tokenizer()
+            tokenizer = self.get_tokenizer()
-        #
+
-        #     if tokenizer.add_special_tokens_sequence_pair.__qualname__.split('.')[0] != "PreTrainedTokenizer":
+            if tokenizer.add_special_tokens_sequence_pair.__qualname__.split('.')[0] != "PreTrainedTokenizer":
-        #         seq_0 = "Test this method."
+                seq_0 = "Test this method."
-        #         seq_1 = "With these inputs."
+                seq_1 = "With these inputs."
-        #         information = tokenizer.encode_plus(seq_0, seq_1, add_special_tokens=True, output_mask=True)
+                information = tokenizer.encode_plus(seq_0, seq_1, add_special_tokens=True, output_mask=True)
-        #         sequences, mask = information["sequence"], information["mask"]
+                sequences, mask = information["sequence"], information["mask"]
-        #         assert len(sequences) == len(mask)
+                assert len(sequences) == len(mask)
 
         def test_number_of_added_tokens(self):
             tokenizer = self.get_tokenizer()

pytorch_transformers/tokenization_bert.py

@@ -204,6 +204,18 @@ class BertTokenizer(PreTrainedTokenizer):
 
         return cls + token_ids_0 + sep + token_ids_1 + sep
 
+    def create_mask_from_sequences(self, sequence_0, sequence_1):
+        """
+        Creates a mask from the two sequences passed to be used in a sequence-pair classification task.
+        A BERT sequence pair mask has the following format:
+        0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1
+        | first sequence    | second sequence
+        """
+        sep = [self.sep_token_id]
+        cls = [self.cls_token_id]
+
+        return len(cls + self.encode(sequence_0) + sep) * [0] + len(self.encode(sequence_1) + sep) * [1]
+
     def save_vocabulary(self, vocab_path):
         """Save the tokenizer vocabulary to a directory or file."""
         index = 0

pytorch_transformers/tokenization_distilbert.py

@@ -64,12 +64,18 @@ class DistilBertTokenizer(BertTokenizer):
     def add_special_tokens_single_sequence(self, token_ids):
         return token_ids
 
-    def add_special_tokens_sequence_pair(self, token_ids_0, token_ids_1, output_mask=False):
+    def add_special_tokens_sequence_pair(self, token_ids_0, token_ids_1):
         sep = [self.sep_token_id]
-        if output_mask:
-            return (
-                token_ids_0 + sep + token_ids_1,
-                [0] * len(token_ids_0 + sep) + [1] * len(token_ids_1)
-            )
-        else:
         return token_ids_0 + sep + token_ids_1
+
+    def create_mask_from_sequences(self, sequence_0, sequence_1):
+        """
+        Creates a mask from the two sequences passed to be used in a sequence-pair classification task.
+        A BERT sequence pair mask has the following format:
+        0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1
+        | first sequence    | second sequence
+        """
+        sep = [self.sep_token_id]
+        cls = [self.cls_token_id]
+
+        return len(self.encode(sequence_0) + sep) * [0] + len(self.encode(sequence_1)) * [1]

pytorch_transformers/tokenization_roberta.py

@@ -96,3 +96,15 @@ class RobertaTokenizer(GPT2Tokenizer):
         sep = [self.sep_token_id]
         cls = [self.cls_token_id]
         return cls + token_ids_0 + sep + sep + token_ids_1 + sep
+
+    def create_mask_from_sequences(self, sequence_0, sequence_1):
+        """
+        Creates a mask from the two sequences passed to be used in a sequence-pair classification task.
+        A RoBERTa sequence pair mask has the following format:
+        0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1
+        | first sequence    | second sequence
+        """
+        sep = [self.sep_token_id]
+        cls = [self.cls_token_id]
+
+        return len(cls + self.encode(sequence_0) + sep + sep) * [0] + len(self.encode(sequence_1) + sep) * [1]

pytorch_transformers/tokenization_utils.py

@@ -779,8 +779,8 @@ class PreTrainedTokenizer(object):
                     second_sentence_tokens
                 )
 
-                # if output_mask:
+                if output_mask:
-                #     sequence, information["mask"] = encoded_sequence
+                    information["mask"] = self.create_mask_from_sequences(text, text_pair)
 
                 information["sequence"] = sequence
             else:
@@ -797,6 +797,10 @@ class PreTrainedTokenizer(object):
 
         return information
 
+    def create_mask_from_sequences(self, sequence_0, sequence_1):
+        logger.warning("This tokenizer does not make use of special tokens.")
+        return [0] * len(self.encode(sequence_0)) + [1] * len(self.encode(sequence_1))
+
     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

pytorch_transformers/tokenization_xlm.py

@@ -770,6 +770,18 @@ class XLMTokenizer(PreTrainedTokenizer):
         cls = [self.cls_token_id]
         return cls + token_ids_0 + sep + token_ids_1 + sep
 
+    def create_mask_from_sequences(self, sequence_0, sequence_1):
+        """
+        Creates a mask from the two sequences passed to be used in a sequence-pair classification task.
+        An XLM sequence pair mask has the following format:
+        0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1
+        | first sequence    | second sequence
+        """
+        sep = [self.sep_token_id]
+        cls = [self.cls_token_id]
+
+        return len(cls + self.encode(sequence_0) + sep) * [0] + len(self.encode(sequence_1) + sep) * [1]
+
     def save_vocabulary(self, save_directory):
         """Save the tokenizer vocabulary and merge files to a directory."""
         if not os.path.isdir(save_directory):

pytorch_transformers/tokenization_xlnet.py

@@ -198,9 +198,21 @@ class XLNetTokenizer(PreTrainedTokenizer):
 
         sep = [self.sep_token_id]
         cls = [self.cls_token_id]
-        cls_segment_ids = [2]
         return token_ids_0 + sep + token_ids_1 + sep + cls
 
+    def create_mask_from_sequences(self, sequence_0, sequence_1):
+        """
+        Creates a mask from the two sequences passed to be used in a sequence-pair classification task.
+        A BERT sequence pair mask has the following format:
+        0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 2
+        | first sequence    | second sequence     | CLS segment ID
+        """
+        sep = [self.sep_token_id]
+        cls = [self.cls_token_id]
+        cls_segment_id = [2]
+
+        return len(self.encode(sequence_0) + sep) * [0] + len(self.encode(sequence_1) + sep) * [1] + cls_segment_id
+
     def save_vocabulary(self, save_directory):
         """ Save the sentencepiece vocabulary (copy original file) and special tokens file
             to a directory.