BertTokenizerFast

src/transformers/__init__.py

@@ -103,7 +103,7 @@ from .pipelines import (
 )
 from .tokenization_albert import AlbertTokenizer
 from .tokenization_auto import AutoTokenizer
-from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer
+from .tokenization_bert import BasicTokenizer, BertTokenizer, BertTokenizerFast, WordpieceTokenizer
 from .tokenization_bert_japanese import BertJapaneseTokenizer, CharacterTokenizer, MecabTokenizer
 from .tokenization_camembert import CamembertTokenizer
 from .tokenization_ctrl import CTRLTokenizer

src/transformers/tokenization_bert.py

@@ -20,7 +20,7 @@ import logging
 import os
 import unicodedata
 
-from .tokenization_utils import PreTrainedTokenizer
+from .tokenization_utils import PreTrainedTokenizer, FastPreTrainedTokenizer
 
 
 logger = logging.getLogger(__name__)
@@ -525,3 +525,54 @@ def _is_punctuation(char):
     if cat.startswith("P"):
         return True
     return False
+
+class BertTokenizerFast(FastPreTrainedTokenizer):
+    vocab_files_names = VOCAB_FILES_NAMES
+    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
+    pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
+    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
+
+    def __init__(self, vocab_file, do_lower_case=True, do_basic_tokenize=True, never_split=None,
+                 unk_token="[UNK]", sep_token="[SEP]", pad_token="[PAD]", cls_token="[CLS]",
+                 mask_token="[MASK]", tokenize_chinese_chars=True,
+                 max_length=None, pad_to_max_length=False, stride=0,
+                 truncation_strategy='longest_first', add_special_tokens=True, **kwargs):
+
+        try:
+            from tokenizers import Tokenizer, models, pre_tokenizers, decoders, processors
+            super(BertTokenizerFast, self).__init__(unk_token=unk_token, sep_token=sep_token,
+                                                    pad_token=pad_token, cls_token=cls_token,
+                                                    mask_token=mask_token, **kwargs)
+
+            self._tokenizer = Tokenizer(models.WordPiece.from_files(
+                vocab_file,
+                unk_token=unk_token
+            ))
+            self._update_special_tokens()
+            self._tokenizer.with_pre_tokenizer(pre_tokenizers.BertPreTokenizer.new(
+                do_basic_tokenize=do_basic_tokenize,
+                do_lower_case=do_lower_case,
+                tokenize_chinese_chars=tokenize_chinese_chars,
+                never_split=never_split if never_split is not None else [],
+            ))
+            self._tokenizer.with_decoder(decoders.WordPiece.new())
+
+            if add_special_tokens:
+                self._tokenizer.with_post_processor(processors.BertProcessing.new(
+                    (sep_token, self._tokenizer.token_to_id(sep_token)),
+                    (cls_token, self._tokenizer.token_to_id(cls_token)),
+                ))
+            if max_length is not None:
+                self._tokenizer.with_truncation(max_length, stride, truncation_strategy)
+            self._tokenizer.with_padding(
+                max_length if pad_to_max_length else None,
+                self.padding_side,
+                self.pad_token_id,
+                self.pad_token_type_id,
+                self.pad_token
+            )
+            self._decoder = decoders.WordPiece.new()
+
+        except (AttributeError, ImportError) as e:
+            logger.error("Make sure you installed `tokenizers` with `pip install tokenizers==0.0.8`")
+            raise e
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