def __init__(
self,
vocab_file: Optional[str] = None,
add_special_tokens: bool = True,
unk_token: str = "[UNK]",
sep_token: str = "[SEP]",
cls_token: str = "[CLS]",
clean_text: bool = True,
handle_chinese_chars: bool = True,
strip_accents: bool = True,
lowercase: bool = True,
wordpieces_prefix: str = "##",
):
if vocab_file is not None:
tokenizer = Tokenizer(
WordPiece.from_files(vocab_file, unk_token=unk_token))
else:
tokenizer = Tokenizer(WordPiece.empty())
tokenizer.add_special_tokens([unk_token, sep_token, cls_token])
tokenizer.normalizer = BertNormalizer(
clean_text=clean_text,
handle_chinese_chars=handle_chinese_chars,
strip_accents=strip_accents,
lowercase=lowercase,
)
tokenizer.pre_tokenizer = BertPreTokenizer()
if add_special_tokens and vocab_file is not None:
sep_token_id = tokenizer.token_to_id(sep_token)
if sep_token_id is None:
raise TypeError("sep_token not found in the vocabulary")
cls_token_id = tokenizer.token_to_id(cls_token)
if cls_token_id is None:
raise TypeError("cls_token not found in the vocabulary")
tokenizer.post_processor = BertProcessing(
(sep_token, sep_token_id), (cls_token, cls_token_id))
tokenizer.decoders = decoders.WordPiece(prefix=wordpieces_prefix)
parameters = {
"model": "BertWordPiece",
"add_special_tokens": add_special_tokens,
"unk_token": unk_token,
"sep_token": sep_token,
"cls_token": cls_token,
"clean_text": clean_text,
"handle_chinese_chars": handle_chinese_chars,
"strip_accents": strip_accents,
"lowercase": lowercase,
"wordpieces_prefix": wordpieces_prefix,
}
super().__init__(tokenizer, parameters)
def __init__(
self,
vocab_file: Optional[str] = None,
unk_token: Union[str, AddedToken] = "[UNK]",
sep_token: Union[str, AddedToken] = "[SEP]",
cls_token: Union[str, AddedToken] = "[CLS]",
pad_token: Union[str, AddedToken] = "[PAD]",
mask_token: Union[str, AddedToken] = "[MASK]",
clean_text: bool = True,
handle_chinese_chars: bool = True,
strip_accents: bool = True,
lowercase: bool = True,
wordpieces_prefix: str = "##",
):
if vocab_file is not None:
tokenizer = Tokenizer(
WordPiece.from_files(vocab_file, unk_token=str(unk_token)))
else:
tokenizer = Tokenizer(WordPiece.empty())
# Let the tokenizer know about special tokens if they are part of the vocab
if tokenizer.token_to_id(str(unk_token)) is not None:
tokenizer.add_special_tokens([str(unk_token)])
if tokenizer.token_to_id(str(sep_token)) is not None:
tokenizer.add_special_tokens([str(sep_token)])
if tokenizer.token_to_id(str(cls_token)) is not None:
tokenizer.add_special_tokens([str(cls_token)])
if tokenizer.token_to_id(str(pad_token)) is not None:
tokenizer.add_special_tokens([str(pad_token)])
if tokenizer.token_to_id(str(mask_token)) is not None:
tokenizer.add_special_tokens([str(mask_token)])
tokenizer.normalizer = BertNormalizer(
clean_text=clean_text,
handle_chinese_chars=handle_chinese_chars,
strip_accents=strip_accents,
lowercase=lowercase,
)
tokenizer.pre_tokenizer = BertPreTokenizer()
if vocab_file is not None:
sep_token_id = tokenizer.token_to_id(str(sep_token))
if sep_token_id is None:
raise TypeError("sep_token not found in the vocabulary")
cls_token_id = tokenizer.token_to_id(str(cls_token))
if cls_token_id is None:
raise TypeError("cls_token not found in the vocabulary")
tokenizer.post_processor = BertProcessing(
(str(sep_token), sep_token_id), (str(cls_token), cls_token_id))
tokenizer.decoder = decoders.WordPiece(prefix=wordpieces_prefix)
parameters = {
"model": "BertWordPiece",
"unk_token": unk_token,
"sep_token": sep_token,
"cls_token": cls_token,
"pad_token": pad_token,
"mask_token": mask_token,
"clean_text": clean_text,
"handle_chinese_chars": handle_chinese_chars,
"strip_accents": strip_accents,
"lowercase": lowercase,
"wordpieces_prefix": wordpieces_prefix,
}
super().__init__(tokenizer, parameters)
def test_instantiate(self, bert_files):
assert isinstance(WordPiece.empty(), Model)
assert isinstance(WordPiece.from_files(bert_files["vocab"]), Model)
