Merge pull request #325 from john-hewitt/master

add BertTokenizer flag to skip basic tokenization

README.md

@@ -507,7 +507,7 @@ where
 Examples:
 ```python
 # BERT
-tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True)
+tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True, do_basic_tokenize=True)
 model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
 
 # OpenAI GPT
@@ -803,11 +803,12 @@ This model *outputs*:
 
 `BertTokenizer` perform end-to-end tokenization, i.e. basic tokenization followed by WordPiece tokenization.
 
-This class has four arguments:
+This class has five arguments:
 
 - `vocab_file`: path to a vocabulary file.
 - `do_lower_case`: convert text to lower-case while tokenizing. **Default = True**.
 - `max_len`: max length to filter the input of the Transformer. Default to pre-trained value for the model if `None`. **Default = None**
+- `do_basic_tokenize`: Do basic tokenization before wordpice tokenization. Set to false if text is pre-tokenized. **Default = True**.
 - `never_split`: a list of tokens that should not be splitted during tokenization. **Default = `["[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]"]`**
 
 and three methods:

pytorch_pretrained_bert/tokenization.py

@@ -74,8 +74,22 @@ def whitespace_tokenize(text):
 class BertTokenizer(object):
     """Runs end-to-end tokenization: punctuation splitting + wordpiece"""
 
-    def __init__(self, vocab_file, do_lower_case=True, max_len=None,
+    def __init__(self, vocab_file, do_lower_case=True, max_len=None, do_basic_tokenize=True,
                  never_split=("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]")):
+        """Constructs a BertTokenizer.
+
+        Args:
+          vocab_file: Path to a one-wordpiece-per-line vocabulary file
+          do_lower_case: Whether to lower case the input
+                         Only has an effect when do_wordpiece_only=False
+          do_basic_tokenize: Whether to do basic tokenization before wordpiece.
+          max_len: An artificial maximum length to truncate tokenized sequences to;
+                         Effective maximum length is always the minimum of this
+                         value (if specified) and the underlying BERT model's
+                         sequence length.
+          never_split: List of tokens which will never be split during tokenization.
+                         Only has an effect when do_wordpiece_only=False
+        """
         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 "
@@ -83,16 +97,21 @@ class BertTokenizer(object):
         self.vocab = load_vocab(vocab_file)
         self.ids_to_tokens = collections.OrderedDict(
             [(ids, tok) for tok, ids in self.vocab.items()])
-        self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case,
-                                              never_split=never_split)
+        self.do_basic_tokenize = do_basic_tokenize
+        if do_basic_tokenize:
+          self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case,
+                                                never_split=never_split)
         self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
         self.max_len = max_len if max_len is not None else int(1e12)
 
     def tokenize(self, text):
-        split_tokens = []
-        for token in self.basic_tokenizer.tokenize(text):
-            for sub_token in self.wordpiece_tokenizer.tokenize(token):
-                split_tokens.append(sub_token)
+        if self.do_basic_tokenize:
+          split_tokens = []
+          for token in self.basic_tokenizer.tokenize(text):
+              for sub_token in self.wordpiece_tokenizer.tokenize(token):
+                  split_tokens.append(sub_token)
+        else:
+          split_tokens = self.wordpiece_tokenizer.tokenize(text)
         return split_tokens
 
     def convert_tokens_to_ids(self, tokens):