def __init__(self,
pretrained_model_name: Optional[str] = None,
cache_dir: Optional[str] = None,
hparams=None):
# SpanBERT checkpoint files do not include vocabulary file, use
# standard BERT directly when user use the pre-trained SpanBERT.
if pretrained_model_name is not None:
if pretrained_model_name.startswith('spanbert'):
pretrained_model_name = pretrained_model_name.lstrip('span')
elif hparams is not None:
hparams = HParams(hparams, None)
if hparams.pretrained_model_name is not None and \
hparams.pretrained_model_name.startswith('spanbert'):
pretrained_model_name = \
hparams.pretrained_model_name.lstrip('span')
self.load_pretrained_config(pretrained_model_name, cache_dir, hparams)
super().__init__(hparams=None)
self.config = {
'tokenize_chinese_chars': self.hparams['tokenize_chinese_chars'],
'do_lower_case': self.hparams['do_lower_case'],
'do_basic_tokenize': self.hparams['do_basic_tokenize'],
'non_split_tokens': self.hparams['non_split_tokens'],
}
if self.pretrained_model_dir is not None:
assert self.pretrained_model_name is not None
vocab_file = os.path.join(self.pretrained_model_dir,
self._VOCAB_FILE_MAP['vocab_file']
[self.pretrained_model_name])
if self._MAX_INPUT_SIZE.get(self.pretrained_model_name):
self.max_len = self._MAX_INPUT_SIZE[self.pretrained_model_name]
else:
vocab_file = self.hparams['vocab_file']
if self.hparams.get('max_len'):
self.max_len = self.hparams['max_len']
if not os.path.isfile(vocab_file):
raise ValueError("Can't find a vocabulary file at path "
"'{}".format(vocab_file))
self.vocab = load_vocab(vocab_file)
self.ids_to_tokens = dict((ids, tok) for tok, ids in self.vocab.items())
self.do_basic_tokenize = self.hparams['do_basic_tokenize']
if self.do_basic_tokenize:
self.basic_tokenizer = BasicTokenizer(
do_lower_case=self.hparams["do_lower_case"],
never_split=self.hparams["non_split_tokens"],
tokenize_chinese_chars=self.hparams["tokenize_chinese_chars"])
self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab,
unk_token=self.unk_token)
def test_wordpiece_tokenizer(self):
vocab_tokens = [
"[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un",
"runn", "##ing"
]
vocab = {}
for (i, token) in enumerate(vocab_tokens):
vocab[token] = i
tokenizer = WordpieceTokenizer(vocab=vocab, unk_token="[UNK]")
self.assertListEqual(tokenizer.tokenize(""), [])
self.assertListEqual(tokenizer.tokenize_with_span(""), [])
self.assertListEqual(tokenizer.tokenize("unwanted running"),
["un", "##want", "##ed", "runn", "##ing"])
self.assertListEqual(tokenizer.tokenize_with_span("unwanted running"),
[('un', 0, 2), ('##want', 2, 6), ('##ed', 6, 8),
('runn', 0, 4), ('##ing', 4, 7)])
self.assertListEqual(tokenizer.tokenize("unwantedX running"),
["[UNK]", "runn", "##ing"])
self.assertListEqual(tokenizer.tokenize_with_span("unwantedX running"),
[('[UNK]', 0, 9), ('runn', 0, 4),
('##ing', 4, 7)])
def __init__(self,
pretrained_model_name: Optional[str] = None,
cache_dir: Optional[str] = None,
hparams=None):
self.load_pretrained_config(pretrained_model_name, cache_dir, hparams)
super().__init__(hparams=None)
self.config = {
'tokenize_chinese_chars': self.hparams['tokenize_chinese_chars'],
'do_lower_case': self.hparams['do_lower_case'],
'do_basic_tokenize': self.hparams['do_basic_tokenize'],
'non_split_tokens': self.hparams['non_split_tokens'],
}
if self.pretrained_model_dir is not None:
if hparams is not None and 'vocab_file' in self.hparams.keys():
vocab_file = hparams['vocab_file']
else:
vocab_file = os.path.join(self.pretrained_model_dir,
self._VOCAB_FILE_NAMES['vocab_file'])
assert self.pretrained_model_name is not None
if self._MAX_INPUT_SIZE.get(self.pretrained_model_name):
self.max_len = self._MAX_INPUT_SIZE[self.pretrained_model_name]
else:
vocab_file = self.hparams['vocab_file']
if self.hparams.get('max_len'):
self.max_len = self.hparams['max_len']
if not os.path.isfile(vocab_file):
raise ValueError("Can't find a vocabulary file at path "
"'{}".format(vocab_file))
self.vocab = load_vocab(vocab_file)
self.ids_to_tokens = dict(
(ids, tok) for tok, ids in self.vocab.items())
self.do_basic_tokenize = self.hparams['do_basic_tokenize']
if self.do_basic_tokenize:
self.basic_tokenizer = BasicTokenizer(
do_lower_case=self.hparams["do_lower_case"],
never_split=self.hparams["non_split_tokens"],
tokenize_chinese_chars=self.hparams["tokenize_chinese_chars"])
self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab,
unk_token=self.unk_token)
def test_wordpiece_tokenizer(self):
vocab_tokens = [
"[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un",
"runn", "##ing"
]
vocab = {}
for (i, token) in enumerate(vocab_tokens):
vocab[token] = i
tokenizer = WordpieceTokenizer(vocab=vocab, unk_token="[UNK]")
self.assertListEqual(tokenizer.tokenize(""), [])
self.assertListEqual(tokenizer.tokenize("unwanted running"),
["un", "##want", "##ed", "runn", "##ing"])
self.assertListEqual(tokenizer.tokenize("unwantedX running"),
["[UNK]", "runn", "##ing"])
class BERTTokenizer(PretrainedBERTMixin, TokenizerBase):
r"""Pre-trained BERT Tokenizer.
Args:
pretrained_model_name (optional): a `str`, the name of
pre-trained model (e.g., `bert-base-uncased`). Please refer to
:class:`~texar.torch.modules.PretrainedBERTMixin` for
all supported models.
If None, the model name in :attr:`hparams` is used.
cache_dir (optional): the path to a folder in which the
pre-trained models will be cached. If `None` (default),
a default directory (``texar_data`` folder under user's home
directory) will be used.
hparams (dict or HParams, optional): Hyperparameters. Missing
hyperparameter will be set to default values. See
:meth:`default_hparams` for the hyperparameter structure
and default values.
"""
_IS_PRETRAINED = True
_MAX_INPUT_SIZE = {
# Standard BERT
'bert-base-uncased': 512,
'bert-large-uncased': 512,
'bert-base-cased': 512,
'bert-large-cased': 512,
'bert-base-multilingual-uncased': 512,
'bert-base-multilingual-cased': 512,
'bert-base-chinese': 512,
# BioBERT
'biobert-v1.0-pmc': 512,
'biobert-v1.0-pubmed-pmc': 512,
'biobert-v1.0-pubmed': 512,
'biobert-v1.1-pubmed': 512,
# SciBERT
'scibert-scivocab-uncased': 512,
'scibert-scivocab-cased': 512,
'scibert-basevocab-uncased': 512,
'scibert-basevocab-cased': 512,
}
_VOCAB_FILE_NAMES = {'vocab_file': 'vocab.txt'}
_VOCAB_FILE_MAP = {
'vocab_file': {
# Standard BERT
'bert-base-uncased': 'vocab.txt',
'bert-large-uncased': 'vocab.txt',
'bert-base-cased': 'vocab.txt',
'bert-large-cased': 'vocab.txt',
'bert-base-multilingual-uncased': 'vocab.txt',
'bert-base-multilingual-cased': 'vocab.txt',
'bert-base-chinese': 'vocab.txt',
# BioBERT
'biobert-v1.0-pmc': 'vocab.txt',
'biobert-v1.0-pubmed-pmc': 'vocab.txt',
'biobert-v1.0-pubmed': 'vocab.txt',
'biobert-v1.1-pubmed': 'vocab.txt',
# SciBERT
'scibert-scivocab-uncased': 'vocab.txt',
'scibert-scivocab-cased': 'vocab.txt',
'scibert-basevocab-uncased': 'vocab.txt',
'scibert-basevocab-cased': 'vocab.txt',
}
}
def __init__(self,
pretrained_model_name: Optional[str] = None,
cache_dir: Optional[str] = None,
hparams=None):
self.load_pretrained_config(pretrained_model_name, cache_dir, hparams)
super().__init__(hparams=None)
self.config = {
'tokenize_chinese_chars': self.hparams['tokenize_chinese_chars'],
'do_lower_case': self.hparams['do_lower_case'],
'do_basic_tokenize': self.hparams['do_basic_tokenize'],
'non_split_tokens': self.hparams['non_split_tokens'],
}
if self.pretrained_model_dir is not None:
assert self.pretrained_model_name is not None
vocab_file = os.path.join(
self.pretrained_model_dir,
self._VOCAB_FILE_MAP['vocab_file'][self.pretrained_model_name])
if self._MAX_INPUT_SIZE.get(self.pretrained_model_name):
self.max_len = self._MAX_INPUT_SIZE[self.pretrained_model_name]
else:
vocab_file = self.hparams['vocab_file']
if self.hparams.get('max_len'):
self.max_len = self.hparams['max_len']
if not os.path.isfile(vocab_file):
raise ValueError("Can't find a vocabulary file at path "
"'{}".format(vocab_file))
self.vocab = load_vocab(vocab_file)
self.ids_to_tokens = dict(
(ids, tok) for tok, ids in self.vocab.items())
self.do_basic_tokenize = self.hparams['do_basic_tokenize']
if self.do_basic_tokenize:
self.basic_tokenizer = BasicTokenizer(
do_lower_case=self.hparams["do_lower_case"],
never_split=self.hparams["non_split_tokens"],
tokenize_chinese_chars=self.hparams["tokenize_chinese_chars"])
self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab,
unk_token=self.unk_token)
def _map_text_to_token(self, text: str) -> List[str]: # type: ignore
split_tokens = []
if self.do_basic_tokenize:
for token in self.basic_tokenizer.tokenize(
text, never_split=self.all_special_tokens):
assert token is not None
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 save_vocab(self, save_dir: str) -> Tuple[str]:
r"""Save the tokenizer vocabulary to a directory or file."""
index = 0
if os.path.isdir(save_dir):
save_dir = os.path.join(save_dir,
self._VOCAB_FILE_NAMES['vocab_file'])
with open(save_dir, "w", encoding="utf-8") as writer:
for token, token_index in sorted(self.vocab.items(),
key=lambda kv: kv[1]):
if index != token_index:
print(
"Saving vocabulary to {}: vocabulary indices are not "
"consecutive. Please check that the vocabulary is "
"not corrupted!".format(save_dir))
index = token_index
writer.write(token + u'\n')
index += 1
return (save_dir, )
@property
def vocab_size(self) -> int:
return len(self.vocab)
def _map_token_to_id(self, token: str) -> int:
r"""Maps a token to an id using the vocabulary."""
unk_id = self.vocab.get(self.unk_token)
assert unk_id is not None
return self.vocab.get(token, unk_id)
def _map_id_to_token(self, index: int) -> str:
r"""Maps an id to a token using the vocabulary.
"""
return self.ids_to_tokens.get(index, self.unk_token)
def map_token_to_text(self, tokens: List[str]) -> str:
r"""Maps a sequence of tokens (string) to a single string."""
out_string = ' '.join(tokens).replace(' ##', '').strip()
return out_string
def encode_text(self,
text_a: str,
text_b: Optional[str] = None,
max_seq_length: Optional[int] = None) -> \
Tuple[List[int], List[int], List[int]]:
r"""Adds special tokens to a sequence or sequence pair and computes the
corresponding segment ids and input mask for BERT specific tasks.
The sequence will be truncated if its length is larger than
``max_seq_length``.
A BERT sequence has the following format:
`[cls_token]` X `[sep_token]`
A BERT sequence pair has the following format:
`[cls_token]` A `[sep_token]` B `[sep_token]`
Args:
text_a: The first input text.
text_b: The second input text.
max_seq_length: Maximum sequence length.
Returns:
A tuple of `(input_ids, segment_ids, input_mask)`, where
- ``input_ids``: A list of input token ids with added
special token ids.
- ``segment_ids``: A list of segment ids.
- ``input_mask``: A list of mask ids. The mask has 1 for real
tokens and 0 for padding tokens. Only real tokens are
attended to.
"""
if max_seq_length is None:
max_seq_length = self.max_len
cls_token_id = self._map_token_to_id(self.cls_token)
sep_token_id = self._map_token_to_id(self.sep_token)
token_ids_a = self.map_text_to_id(text_a)
assert isinstance(token_ids_a, list)
token_ids_b = None
if text_b:
token_ids_b = self.map_text_to_id(text_b)
if token_ids_b:
assert isinstance(token_ids_b, list)
# Modifies `token_ids_a` and `token_ids_b` in place so that the
# total length is less than the specified length.
# Account for [CLS], [SEP], [SEP] with "- 3"
truncate_seq_pair(token_ids_a, token_ids_b, max_seq_length - 3)
input_ids = ([cls_token_id] + token_ids_a + [sep_token_id] +
token_ids_b + [sep_token_id])
segment_ids = [0] * (len(token_ids_a) + 2) + \
[1] * (len(token_ids_b) + 1)
else:
# Account for [CLS] and [SEP] with "- 2"
token_ids_a = token_ids_a[:max_seq_length - 2]
input_ids = [cls_token_id] + token_ids_a + [sep_token_id]
segment_ids = [0] * len(input_ids)
input_mask = [1] * len(input_ids)
# Zero-pad up to the maximum sequence length.
input_ids = input_ids + [0] * (max_seq_length - len(input_ids))
segment_ids = segment_ids + [0] * (max_seq_length - len(segment_ids))
input_mask = input_mask + [0] * (max_seq_length - len(input_mask))
assert len(input_ids) == max_seq_length
assert len(segment_ids) == max_seq_length
assert len(input_mask) == max_seq_length
return input_ids, segment_ids, input_mask
@staticmethod
def default_hparams() -> Dict[str, Any]:
r"""Returns a dictionary of hyperparameters with default values.
* The tokenizer is determined by the constructor argument
:attr:`pretrained_model_name` if it's specified. In this case,
`hparams` are ignored.
* Otherwise, the tokenizer is determined by
`hparams['pretrained_model_name']` if it's specified. All other
configurations in `hparams` are ignored.
* If the above two are `None`, the tokenizer is defined by the
configurations in `hparams`.
.. code-block:: python
{
"pretrained_model_name": "bert-base-uncased",
"vocab_file": None,
"max_len": 512,
"unk_token": "[UNK]",
"sep_token": "[SEP]",
"pad_token": "[PAD]",
"cls_token": "[CLS]",
"mask_token": "[MASK]",
"tokenize_chinese_chars": True,
"do_lower_case": True,
"do_basic_tokenize": True,
"non_split_tokens": None,
"name": "bert_tokenizer",
}
Here:
`"pretrained_model_name"`: str or None
The name of the pre-trained BERT model.
`"vocab_file"`: str or None
The path to a one-wordpiece-per-line vocabulary file.
`"max_len"`: int
The maximum sequence length that this model might ever be used with.
`"unk_token"`: str
Unknown token.
`"sep_token"`: str
Separation token.
`"pad_token"`: str
Padding token.
`"cls_token"`: str
Classification token.
`"mask_token"`: str
Masking token.
`"tokenize_chinese_chars"`: bool
Whether to tokenize Chinese characters.
`"do_lower_case"`: bool
Whether to lower case the input
Only has an effect when `do_basic_tokenize=True`
`"do_basic_tokenize"`: bool
Whether to do basic tokenization before wordpiece.
`"non_split_tokens"`: list
List of tokens which will never be split during tokenization.
Only has an effect when `do_basic_tokenize=True`
`"name"`: str
Name of the tokenizer.
"""
return {
'pretrained_model_name': 'bert-base-uncased',
'vocab_file': None,
'max_len': 512,
'unk_token': '[UNK]',
'sep_token': '[SEP]',
'pad_token': '[PAD]',
'cls_token': '[CLS]',
'mask_token': '[MASK]',
'tokenize_chinese_chars': True,
'do_lower_case': True,
'do_basic_tokenize': True,
'non_split_tokens': None,
'name': 'bert_tokenizer',
'@no_typecheck': ['pretrained_model_name'],
}
@classmethod
def _transform_config(cls, pretrained_model_name: str, cache_dir: str):
r"""Returns the configuration of the pre-trained BERT tokenizer."""
return {
'vocab_file': None,
'max_len': 512,
'unk_token': '[UNK]',
'sep_token': '[SEP]',
'pad_token': '[PAD]',
'cls_token': '[CLS]',
'mask_token': '[MASK]',
'tokenize_chinese_chars': True,
'do_lower_case': True,
'do_basic_tokenize': True,
'non_split_tokens': None,
}