max_len_single_sentence & max_len_sentences_pair as attributes so they can be modified

pytorch_transformers/tokenization_bert.py

@@ -125,6 +125,9 @@ class BertTokenizer(PreTrainedTokenizer):
         super(BertTokenizer, self).__init__(unk_token=unk_token, sep_token=sep_token,
                                             pad_token=pad_token, cls_token=cls_token,
                                             mask_token=mask_token, **kwargs)
+        self.max_len_single_sentence = self.max_len - 2  # take into account special tokens
+        self.max_len_sentences_pair = self.max_len - 3  # take into account special tokens
+
         if not os.path.isfile(vocab_file):
             raise ValueError(
                 "Can't find a vocabulary file at path '{}'. To load the vocabulary from a Google pretrained "
@@ -139,14 +142,6 @@ class BertTokenizer(PreTrainedTokenizer):
                                                   tokenize_chinese_chars=tokenize_chinese_chars)
         self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=self.unk_token)
 
-    @property
-    def max_len_single_sentence(self):
-        return self.max_len - 2  # take into account special tokens
-
-    @property
-    def max_len_sentences_pair(self):
-        return self.max_len - 3  # take into account special tokens
-
     @property
     def vocab_size(self):
         return len(self.vocab)

pytorch_transformers/tokenization_gpt2.py

@@ -108,6 +108,8 @@ class GPT2Tokenizer(PreTrainedTokenizer):
     def __init__(self, vocab_file, merges_file, errors='replace', unk_token="<|endoftext|>",
                  bos_token="<|endoftext|>", eos_token="<|endoftext|>", **kwargs):
         super(GPT2Tokenizer, self).__init__(bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, **kwargs)
+        self.max_len_single_sentence = self.max_len # no default special tokens - you can update this value if you add special tokens
+        self.max_len_sentences_pair = self.max_len # no default special tokens - you can update this value if you add special tokens
 
         self.encoder = json.load(open(vocab_file))
         self.decoder = {v:k for k,v in self.encoder.items()}

pytorch_transformers/tokenization_openai.py

@@ -87,6 +87,9 @@ class OpenAIGPTTokenizer(PreTrainedTokenizer):
     def __init__(self, vocab_file, merges_file, unk_token="<unk>", **kwargs):
         super(OpenAIGPTTokenizer, self).__init__(unk_token=unk_token, **kwargs)
 
+        self.max_len_single_sentence = self.max_len # no default special tokens - you can update this value if you add special tokens
+        self.max_len_sentences_pair = self.max_len # no default special tokens - you can update this value if you add special tokens
+
         try:
             import ftfy
             from spacy.lang.en import English

pytorch_transformers/tokenization_roberta.py

@@ -77,6 +77,9 @@ class RobertaTokenizer(PreTrainedTokenizer):
                                                sep_token=sep_token, cls_token=cls_token, pad_token=pad_token,
                                                mask_token=mask_token, **kwargs)
 
+        self.max_len_single_sentence = self.max_len - 2  # take into account special tokens
+        self.max_len_sentences_pair = self.max_len - 4  # take into account special tokens
+
         self.encoder = json.load(open(vocab_file, encoding="utf-8"))
         self.decoder = {v: k for k, v in self.encoder.items()}
         self.errors = errors  # how to handle errors in decoding
@@ -160,14 +163,6 @@ class RobertaTokenizer(PreTrainedTokenizer):
         text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors=self.errors)
         return text
 
-    @property
-    def max_len_single_sentence(self):
-        return self.max_len - 2  # take into account special tokens
-
-    @property
-    def max_len_sentences_pair(self):
-        return self.max_len - 4  # take into account special tokens
-
     def add_special_tokens_single_sentence(self, token_ids):
         """
         Adds special tokens to a sequence for sequence classification tasks.

pytorch_transformers/tokenization_transfo_xl.py

@@ -73,6 +73,10 @@ class TransfoXLTokenizer(PreTrainedTokenizer):
         super(TransfoXLTokenizer, self).__init__(unk_token=unk_token, eos_token=eos_token,
                                                  additional_special_tokens=additional_special_tokens,
                                                  **kwargs)
+
+        self.max_len_single_sentence = self.max_len # no default special tokens - you can update this value if you add special tokens
+        self.max_len_sentences_pair = self.max_len # no default special tokens - you can update this value if you add special tokens
+
         if never_split is None:
             never_split = self.all_special_tokens
         if special is None:

pytorch_transformers/tokenization_utils.py

@@ -67,14 +67,6 @@ class PreTrainedTokenizer(object):
                                  "pad_token", "cls_token", "mask_token",
                                  "additional_special_tokens"]
 
-    @property
-    def max_len_single_sentence(self):
-        return self.max_len  # Default to max_len but can be smaller in specific tokenizers to take into account special tokens
-
-    @property
-    def max_len_sentences_pair(self):
-        return self.max_len  # Default to max_len but can be smaller in specific tokenizers to take into account special tokens
-
     @property
     def bos_token(self):
         """ Beginning of sentence token (string). Log an error if used while not having been set. """
@@ -174,6 +166,9 @@ class PreTrainedTokenizer(object):
         self._additional_special_tokens = []
 
         self.max_len = max_len if max_len is not None else int(1e12)
+        self.max_len_single_sentence = self.max_len
+        self.max_len_sentences_pair = self.max_len
+
         self.added_tokens_encoder = {}
         self.added_tokens_decoder = {}
 

pytorch_transformers/tokenization_xlm.py

@@ -122,6 +122,10 @@ class XLMTokenizer(PreTrainedTokenizer):
                                            cls_token=cls_token, mask_token=mask_token,
                                            additional_special_tokens=additional_special_tokens,
                                            **kwargs)
+
+        self.max_len_single_sentence = self.max_len - 2  # take into account special tokens
+        self.max_len_sentences_pair = self.max_len - 3  # take into account special tokens
+
         try:
             import ftfy
             from spacy.lang.en import English
@@ -215,14 +219,6 @@ class XLMTokenizer(PreTrainedTokenizer):
         out_string = ''.join(tokens).replace('</w>', ' ').strip()
         return out_string
 
-    @property
-    def max_len_single_sentence(self):
-        return self.max_len - 2  # take into account special tokens
-
-    @property
-    def max_len_sentences_pair(self):
-        return self.max_len - 3  # take into account special tokens
-
     def add_special_tokens_single_sentence(self, token_ids):
         """
         Adds special tokens to a sequence for sequence classification tasks.

pytorch_transformers/tokenization_xlnet.py

@@ -71,6 +71,10 @@ class XLNetTokenizer(PreTrainedTokenizer):
                                              pad_token=pad_token, cls_token=cls_token,
                                              mask_token=mask_token, additional_special_tokens=
                                              additional_special_tokens, **kwargs)
+
+        self.max_len_single_sentence = self.max_len - 2  # take into account special tokens
+        self.max_len_sentences_pair = self.max_len - 3  # take into account special tokens
+
         try:
             import sentencepiece as spm
         except ImportError:
@@ -177,14 +181,6 @@ class XLNetTokenizer(PreTrainedTokenizer):
         out_string = ''.join(tokens).replace(SPIECE_UNDERLINE, ' ').strip()
         return out_string
 
-    @property
-    def max_len_single_sentence(self):
-        return self.max_len - 2  # take into account special tokens
-
-    @property
-    def max_len_sentences_pair(self):
-        return self.max_len - 3  # take into account special tokens
-
     def add_special_tokens_single_sentence(self, token_ids):
         """
         Adds special tokens to a sequence pair for sequence classification tasks.