# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import pickle
import six
import shutil
from paddle.utils import try_import
from paddlenlp.utils.env import MODEL_HOME
from .. import BasicTokenizer, PretrainedTokenizer, WordpieceTokenizer
__all__ = ['ErnieTokenizer', 'ErnieTinyTokenizer']
[docs]class ErnieTokenizer(PretrainedTokenizer):
"""
Constructs an ERNIE tokenizer. It uses a basic tokenizer to do punctuation
splitting, lower casing and so on, and follows a WordPiece tokenizer to
tokenize as subwords.
Args:
vocab_file (str): file path of the vocabulary
do_lower_case (bool): Whether the text strips accents and convert to
lower case. Default: `True`.
Default: True.
unk_token (str): The special token for unkown words. Default: "[UNK]".
sep_token (str): The special token for separator token . Default: "[SEP]".
pad_token (str): The special token for padding. Default: "[PAD]".
cls_token (str): The special token for cls. Default: "[CLS]".
mask_token (str): The special token for mask. Default: "[MASK]".
Examples:
.. code-block:: python
from paddlenlp.transformers import ErnieTokenizer
tokenizer = ErnieTokenizer.from_pretrained('ernie')
# the following line get: ['he', 'was', 'a', 'puppet', '##eer']
tokens = tokenizer('He was a puppeteer')
# the following line get: 'he was a puppeteer'
tokenizer.convert_tokens_to_string(tokens)
"""
resource_files_names = {"vocab_file": "vocab.txt"} # for save_pretrained
pretrained_resource_files_map = {
"vocab_file": {
"ernie-1.0":
"https://paddlenlp.bj.bcebos.com/models/transformers/ernie/vocab.txt",
"ernie-2.0-en":
"https://paddlenlp.bj.bcebos.com/models/transformers/ernie_v2_base/vocab.txt",
"ernie-2.0-large-en":
"https://paddlenlp.bj.bcebos.com/models/transformers/ernie_v2_large/vocab.txt",
"ernie-gen-base-en":
"https://paddlenlp.bj.bcebos.com/models/transformers/ernie-gen-base-en/vocab.txt",
"ernie-gen-large-en":
"https://paddlenlp.bj.bcebos.com/models/transformers/ernie-gen-large/vocab.txt",
"ernie-gen-large-430g-en":
"https://paddlenlp.bj.bcebos.com/models/transformers/ernie-gen-large-430g/vocab.txt",
}
}
pretrained_init_configuration = {
"ernie-1.0": {
"do_lower_case": True
},
"ernie-2.0-en": {
"do_lower_case": True
},
"ernie-2.0-large-en": {
"do_lower_case": True
},
"ernie-gen-base-en": {
"do_lower_case": True
},
"ernie-gen-large-en": {
"do_lower_case": True
},
"ernie-gen-large-430g-en": {
"do_lower_case": True
},
}
def __init__(self,
vocab_file,
do_lower_case=True,
unk_token="[UNK]",
sep_token="[SEP]",
pad_token="[PAD]",
cls_token="[CLS]",
mask_token="[MASK]"):
if not os.path.isfile(vocab_file):
raise ValueError(
"Can't find a vocabulary file at path '{}'. To load the "
"vocabulary from a pretrained model please use "
"`tokenizer = ErnieTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
.format(vocab_file))
self.vocab = self.load_vocabulary(vocab_file, unk_token=unk_token)
self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case)
self.wordpiece_tokenizer = WordpieceTokenizer(
vocab=self.vocab, unk_token=unk_token)
@property
def vocab_size(self):
"""
return the size of vocabulary.
Returns:
int: the size of vocabulary.
"""
return len(self.vocab)
def _tokenize(self, text):
"""
End-to-end tokenization for ERNIE models.
Args:
text (str): The text to be tokenized.
Returns:
list: A list of string representing converted tokens.
"""
split_tokens = []
for token in self.basic_tokenizer.tokenize(text):
for sub_token in self.wordpiece_tokenizer.tokenize(token):
split_tokens.append(sub_token)
return split_tokens
[docs] def tokenize(self, text):
"""
End-to-end tokenization for ERNIE models.
Args:
text (str): The text to be tokenized.
Returns:
list: A list of string representing converted tokens.
"""
return self._tokenize(text)
[docs] def convert_tokens_to_string(self, tokens):
"""
Converts a sequence of tokens (list of string) in a single string. Since
the usage of WordPiece introducing `##` to concat subwords, also remove
`##` when converting.
Args:
tokens (list): A list of string representing tokens to be converted.
Returns:
str: Converted string from tokens.
"""
out_string = " ".join(tokens).replace(" ##", "").strip()
return out_string
[docs] def num_special_tokens_to_add(self, pair=False):
"""
Returns the number of added tokens when encoding a sequence with special tokens.
Note:
This encodes inputs and checks the number of added tokens, and is therefore not efficient. Do not put this
inside your training loop.
Args:
pair: Returns the number of added tokens in the case of a sequence pair if set to True, returns the
number of added tokens in the case of a single sequence if set to False.
Returns:
Number of tokens added to sequences
"""
token_ids_0 = []
token_ids_1 = []
return len(
self.build_inputs_with_special_tokens(token_ids_0, token_ids_1
if pair else None))
[docs] def build_offset_mapping_with_special_tokens(self,
offset_mapping_0,
offset_mapping_1=None):
"""
Build offset map from a pair of offset map by concatenating and adding offsets of special tokens.
A ERNIE offset_mapping has the following format:
::
- single sequence: ``(0,0) X (0,0)``
- pair of sequences: `(0,0) A (0,0) B (0,0)``
Args:
offset_mapping_ids_0 (:obj:`List[tuple]`):
List of char offsets to which the special tokens will be added.
offset_mapping_ids_1 (:obj:`List[tuple]`, `optional`):
Optional second list of char offsets for offset mapping pairs.
Returns:
:obj:`List[tuple]`: List of char offsets with the appropriate offsets of special tokens.
"""
if offset_mapping_1 is None:
return [(0, 0)] + offset_mapping_0 + [(0, 0)]
return [(0, 0)] + offset_mapping_0 + [(0, 0)
] + offset_mapping_1 + [(0, 0)]
[docs] def create_token_type_ids_from_sequences(self,
token_ids_0,
token_ids_1=None):
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task.
A ERNIE sequence pair mask has the following format:
::
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence | second sequence |
If :obj:`token_ids_1` is :obj:`None`, this method only returns the first portion of the mask (0s).
Args:
token_ids_0 (:obj:`List[int]`):
List of IDs.
token_ids_1 (:obj:`List[int]`, `optional`):
Optional second list of IDs for sequence pairs.
Returns:
:obj:`List[int]`: List of token_type_id according to the given sequence(s).
"""
_sep = [self.sep_token_id]
_cls = [self.cls_token_id]
if token_ids_1 is None:
return len(_cls + token_ids_0 + _sep) * [0]
return len(_cls + token_ids_0 + _sep) * [0] + len(token_ids_1 +
_sep) * [1]
[docs]class ErnieTinyTokenizer(PretrainedTokenizer):
"""
Constructs a ErnieTiny tokenizer. It uses the `dict.wordseg.pickle` cut the text to words, and
use the `sentencepiece` tools to cut the words to sub-words.
Args:
vocab_file (str): file path of the vocabulary
do_lower_case (bool): Whether the text strips accents and convert to
lower case. Default: `True`.
unk_token (str): The special token for unkown words. Default: "[UNK]".
sep_token (str): The special token for separator token . Default: "[SEP]".
pad_token (str): The special token for padding. Default: "[PAD]".
cls_token (str): The special token for cls. Default: "[CLS]".
mask_token (str): The special token for mask. Default: "[MASK]".
Examples:
.. code-block:: python
from paddlenlp.transformers import ErnieTinyTokenizer
tokenizer = ErnieTinyTokenizer.from_pretrained('ernie-tiny)
# the following line get: ['he', 'was', 'a', 'puppet', '##eer']
tokens = tokenizer('He was a puppeteer')
# the following line get: 'he was a puppeteer'
tokenizer.convert_tokens_to_string(tokens)
"""
resource_files_names = {
"sentencepiece_model_file": "spm_cased_simp_sampled.model",
"vocab_file": "vocab.txt",
"word_dict": "dict.wordseg.pickle"
} # for save_pretrained
pretrained_resource_files_map = {
"vocab_file": {
"ernie-tiny":
"https://paddlenlp.bj.bcebos.com/models/transformers/ernie_tiny/vocab.txt"
},
"sentencepiece_model_file": {
"ernie-tiny":
"https://paddlenlp.bj.bcebos.com/models/transformers/ernie_tiny/spm_cased_simp_sampled.model"
},
"word_dict": {
"ernie-tiny":
"https://paddlenlp.bj.bcebos.com/models/transformers/ernie_tiny/dict.wordseg.pickle"
},
}
pretrained_init_configuration = {"ernie-tiny": {"do_lower_case": True}}
def __init__(self,
vocab_file,
sentencepiece_model_file,
word_dict,
do_lower_case=True,
encoding="utf8",
unk_token="[UNK]",
sep_token="[SEP]",
pad_token="[PAD]",
cls_token="[CLS]",
mask_token="[MASK]"):
mod = try_import('sentencepiece')
self.sp_model = mod.SentencePieceProcessor()
self.word_dict = word_dict
self.do_lower_case = do_lower_case
self.encoding = encoding
if not os.path.isfile(vocab_file):
raise ValueError(
"Can't find a vocabulary file at path '{}'. To load the "
"vocabulary from a pretrained model please use "
"`tokenizer = ErnieTinyTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
.format(vocab_file))
if not os.path.isfile(word_dict):
raise ValueError(
"Can't find a file at path '{}'. To load the "
"word dict from a pretrained model please use "
"`tokenizer = ErnieTinyTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
.format(word_dict))
self.dict = pickle.load(open(word_dict, 'rb'))
self.vocab = self.load_vocabulary(vocab_file, unk_token=unk_token)
# if the sentencepiece_model_file is not exists, just the default sentence-piece model
if os.path.isfile(sentencepiece_model_file):
self.sp_model.Load(sentencepiece_model_file)
@property
def vocab_size(self):
"""
return the size of vocabulary.
Returns:
int: the size of vocabulary.
"""
return len(self.vocab)
def cut(self, chars):
words = []
idx = 0
window_size = 5
while idx < len(chars):
matched = False
for i in range(window_size, 0, -1):
cand = chars[idx:idx + i]
if cand in self.dict:
words.append(cand)
matched = True
break
if not matched:
i = 1
words.append(chars[idx])
idx += i
return words
def _tokenize(self, text):
"""
End-to-end tokenization for ErnieTiny models.
Args:
text (str): The text to be tokenized.
Returns:
list: A list of string representing converted tokens.
"""
if len(text) == 0:
return []
if not isinstance(text, six.string_types):
text = text.decode(self.encoding)
text = [s for s in self.cut(text) if s != ' ']
text = ' '.join(text)
text = text.lower()
tokens = self.sp_model.EncodeAsPieces(text)
in_vocab_tokens = []
unk_token = self.vocab.unk_token
for token in tokens:
if token in self.vocab:
in_vocab_tokens.append(token)
else:
in_vocab_tokens.append(unk_token)
return in_vocab_tokens
[docs] def tokenize(self, text):
"""
End-to-end tokenization for ERNIE Tiny models.
Args:
text (str): The text to be tokenized.
Returns:
list: A list of string representing converted tokens.
"""
return self._tokenize(text)
[docs] def convert_tokens_to_string(self, tokens):
"""
Converts a sequence of tokens (list of string) in a single string. Since
the usage of WordPiece introducing `##` to concat subwords, also remove
`##` when converting.
Args:
tokens (list): A list of string representing tokens to be converted.
Returns:
str: Converted string from tokens.
"""
out_string = " ".join(tokens).replace(" ##", "").strip()
return out_string
[docs] def save_resources(self, save_directory):
"""
Save tokenizer related resources to files under `save_directory`.
Args:
save_directory (str): Directory to save files into.
"""
for name, file_name in self.resource_files_names.items():
### TODO: make the name 'ernie-tiny' as a variable
source_path = os.path.join(MODEL_HOME, 'ernie-tiny', file_name)
save_path = os.path.join(save_directory,
self.resource_files_names[name])
shutil.copyfile(source_path, save_path)
[docs] def num_special_tokens_to_add(self, pair=False):
"""
Returns the number of added tokens when encoding a sequence with special tokens.
Note:
This encodes inputs and checks the number of added tokens, and is therefore not efficient. Do not put this
inside your training loop.
Args:
pair: Returns the number of added tokens in the case of a sequence pair if set to True, returns the
number of added tokens in the case of a single sequence if set to False.
Returns:
Number of tokens added to sequences
"""
token_ids_0 = []
token_ids_1 = []
return len(
self.build_inputs_with_special_tokens(token_ids_0, token_ids_1
if pair else None))
[docs] def build_offset_mapping_with_special_tokens(self,
offset_mapping_0,
offset_mapping_1=None):
"""
Build offset map from a pair of offset map by concatenating and adding offsets of special tokens.
A ERNIE offset_mapping has the following format:
::
- single sequence: ``(0,0) X (0,0)``
- pair of sequences: `(0,0) A (0,0) B (0,0)``
Args:
offset_mapping_ids_0 (:obj:`List[tuple]`):
List of char offsets to which the special tokens will be added.
offset_mapping_ids_1 (:obj:`List[tuple]`, `optional`):
Optional second list of char offsets for offset mapping pairs.
Returns:
:obj:`List[tuple]`: List of char offsets with the appropriate offsets of special tokens.
"""
if offset_mapping_1 is None:
return [(0, 0)] + offset_mapping_0 + [(0, 0)]
return [(0, 0)] + offset_mapping_0 + [(0, 0)
] + offset_mapping_1 + [(0, 0)]
[docs] def create_token_type_ids_from_sequences(self,
token_ids_0,
token_ids_1=None):
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task.
A ERNIE sequence pair mask has the following format:
::
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence | second sequence |
If :obj:`token_ids_1` is :obj:`None`, this method only returns the first portion of the mask (0s).
Args:
token_ids_0 (:obj:`List[int]`):
List of IDs.
token_ids_1 (:obj:`List[int]`, `optional`):
Optional second list of IDs for sequence pairs.
Returns:
:obj:`List[int]`: List of token_type_id according to the given sequence(s).
"""
_sep = [self.sep_token_id]
_cls = [self.cls_token_id]
if token_ids_1 is None:
return len(_cls + token_ids_0 + _sep) * [0]
return len(_cls + token_ids_0 + _sep) * [0] + len(token_ids_1 +
_sep) * [1]
[docs] def get_special_tokens_mask(self,
token_ids_0,
token_ids_1=None,
already_has_special_tokens=False):
"""
Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer ``encode`` methods.
Args:
token_ids_0 (List[int]): List of ids of the first sequence.
token_ids_1 (List[int], optinal): List of ids of the second sequence.
already_has_special_tokens (bool, optional): Whether or not the token list is already
formatted with special tokens for the model. Defaults to None.
Returns:
results (List[int]): The list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
if token_ids_1 is not None:
raise ValueError(
"You should not supply a second sequence if the provided sequence of "
"ids is already formatted with special tokens for the model."
)
return list(
map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0,
token_ids_0))
if token_ids_1 is not None:
return [1] + ([0] * len(token_ids_0)) + [1] + (
[0] * len(token_ids_1)) + [1]
return [1] + ([0] * len(token_ids_0)) + [1]