# 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
import sentencepiece as spm
from paddlenlp.utils.env import MODEL_HOME
from .. import BasicTokenizer, PretrainedTokenizer, WordpieceTokenizer
__all__ = ['ErnieTokenizer', 'ErnieTinyTokenizer']
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"ernie-1.0": 513,
"ernie-1.0-base-zh": 513,
"ernie-1.0-base-zh-cw": 512,
"ernie-1.0-large-zh-cw": 512,
"ernie-tiny": 600,
"ernie-2.0-base-zh": 513,
"ernie-2.0-large-zh": 512,
"ernie-2.0-base-en": 512,
"ernie-2.0-base-en-finetuned-squad": 512,
"ernie-2.0-large-en": 512,
"ernie-gen-base-en": 1024,
"ernie-gen-large-en": 1024,
"ernie-gen-large-en-430g": 1024,
"rocketqa-zh-dureader-query-encoder": 513,
"rocketqa-zh-dureader-para-encoder": 513,
"rocketqa-v1-marco-query-encoder": 512,
"rocketqa-v1-marco-para-encoder": 512,
"rocketqa-zh-dureader-cross-encoder": 513,
"rocketqa-v1-marco-cross-encoder": 512,
"ernie-3.0-base-zh": 2048,
"ernie-3.0-xbase-zh": 2048,
"ernie-3.0-medium-zh": 2048,
"ernie-3.0-mini-zh": 2048,
"ernie-3.0-micro-zh": 2048,
"ernie-3.0-nano-zh": 2048,
"rocketqa-zh-base-query-encoder": 2048,
"rocketqa-zh-base-para-encoder": 2048,
"rocketqa-zh-medium-query-encoder": 2048,
"rocketqa-zh-medium-para-encoder": 2048,
"rocketqa-zh-mini-query-encoder": 2048,
"rocketqa-zh-mini-para-encoder": 2048,
"rocketqa-zh-micro-query-encoder": 2048,
"rocketqa-zh-micro-para-encoder": 2048,
"rocketqa-zh-nano-query-encoder": 2048,
"rocketqa-zh-nano-para-encoder": 2048,
"rocketqa-base-cross-encoder": 2048,
"rocketqa-medium-cross-encoder": 2048,
"rocketqa-mini-cross-encoder": 2048,
"rocketqa-micro-cross-encoder": 2048,
"rocketqa-nano-cross-encoder": 2048,
"rocketqav2-en-marco-cross-encoder": 512,
"rocketqav2-en-marco-query-encoder": 512,
"rocketqav2-en-marco-para-encoder": 512,
"ernie-search-base-dual-encoder-marco-en": 512,
"ernie-search-large-cross-encoder-marco-en": 512,
}
[文档]class ErnieTokenizer(PretrainedTokenizer):
r"""
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.
This tokenizer inherits from :class:`~paddlenlp.transformers.tokenizer_utils.PretrainedTokenizer`
which contains most of the main methods. For more information regarding those methods,
please refer to this superclass.
Args:
vocab_file (str):
The vocabulary file path (ends with '.txt') required to instantiate
a `WordpieceTokenizer`.
do_lower_case (str, optional):
Whether or not to lowercase the input when tokenizing.
Defaults to`True`.
unk_token (str, optional):
A special token representing the *unknown (out-of-vocabulary)* token.
An unknown token is set to be `unk_token` inorder to be converted to an ID.
Defaults to "[UNK]".
sep_token (str, optional):
A special token separating two different sentences in the same input.
Defaults to "[SEP]".
pad_token (str, optional):
A special token used to make arrays of tokens the same size for batching purposes.
Defaults to "[PAD]".
cls_token (str, optional):
A special token used for sequence classification. It is the last token
of the sequence when built with special tokens. Defaults to "[CLS]".
mask_token (str, optional):
A special token representing a masked token. This is the token used
in the masked language modeling task which the model tries to predict the original unmasked ones.
Defaults to "[MASK]".
Examples:
.. code-block::
from paddlenlp.transformers import ErnieTokenizer
tokenizer = ErnieTokenizer.from_pretrained('ernie-1.0')
encoded_inputs = tokenizer('He was a puppeteer')
# encoded_inputs:
# { 'input_ids': [1, 4444, 4385, 1545, 6712, 10062, 9568, 9756, 9500, 2],
# 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}
# }
"""
resource_files_names = {"vocab_file": "vocab.txt"} # for save_pretrained
pretrained_resource_files_map = {
"vocab_file": {
# Deprecated, alias for ernie-1.0-base-zh
"ernie-1.0":
"https://bj.bcebos.com/paddlenlp/models/transformers/ernie/vocab.txt",
"ernie-1.0-base-zh":
"https://bj.bcebos.com/paddlenlp/models/transformers/ernie/vocab.txt",
"ernie-1.0-base-zh-cw":
"https://bj.bcebos.com/paddlenlp/models/transformers/ernie/ernie_1.0_base_zh_cw_vocab.txt",
"ernie-1.0-large-zh-cw":
"https://bj.bcebos.com/paddlenlp/models/transformers/ernie/vocab.txt",
"ernie-tiny":
"https://bj.bcebos.com/paddlenlp/models/transformers/ernie_tiny/vocab.txt",
"ernie-2.0-base-zh":
"https://bj.bcebos.com/paddlenlp/models/transformers/ernie_2.0/ernie_2.0_base_zh_vocab.txt",
"ernie-2.0-large-zh":
"https://bj.bcebos.com/paddlenlp/models/transformers/ernie_2.0/ernie_2.0_large_zh_vocab.txt",
"ernie-2.0-base-en":
"https://bj.bcebos.com/paddlenlp/models/transformers/ernie_v2_base/vocab.txt",
"ernie-2.0-base-en-finetuned-squad":
"https://bj.bcebos.com/paddlenlp/models/transformers/ernie_v2_base/vocab.txt",
"ernie-2.0-large-en":
"https://bj.bcebos.com/paddlenlp/models/transformers/ernie_v2_large/vocab.txt",
"ernie-gen-base-en":
"https://bj.bcebos.com/paddlenlp/models/transformers/ernie-gen-base-en/vocab.txt",
"ernie-gen-large-en":
"https://bj.bcebos.com/paddlenlp/models/transformers/ernie-gen-large/vocab.txt",
"ernie-gen-large-en-430g":
"https://bj.bcebos.com/paddlenlp/models/transformers/ernie-gen-large-430g/vocab.txt",
"rocketqa-zh-dureader-query-encoder":
"https://bj.bcebos.com/paddlenlp/models/transformers/rocketqa/rocketqa-zh-dureader-vocab.txt",
"rocketqa-zh-dureader-para-encoder":
"https://bj.bcebos.com/paddlenlp/models/transformers/rocketqa/rocketqa-zh-dureader-vocab.txt",
"rocketqa-v1-marco-query-encoder":
"https://bj.bcebos.com/paddlenlp/models/transformers/rocketqa/rocketqa-v1-marco-vocab.txt",
"rocketqa-v1-marco-para-encoder":
"https://bj.bcebos.com/paddlenlp/models/transformers/rocketqa/rocketqa-v1-marco-vocab.txt",
"rocketqa-zh-dureader-cross-encoder":
"https://bj.bcebos.com/paddlenlp/models/transformers/rocketqa/rocketqa-zh-dureader-vocab.txt",
"rocketqa-v1-marco-cross-encoder":
"https://bj.bcebos.com/paddlenlp/models/transformers/rocketqa/rocketqa-v1-marco-vocab.txt",
"ernie-3.0-base-zh":
"https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_base_zh_vocab.txt",
"ernie-3.0-xbase-zh":
"https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_xbase_zh_vocab.txt",
"ernie-3.0-medium-zh":
"https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_medium_zh_vocab.txt",
"ernie-3.0-mini-zh":
"https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_mini_zh_vocab.txt",
"ernie-3.0-micro-zh":
"https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_micro_zh_vocab.txt",
"ernie-3.0-nano-zh":
"https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_nano_zh_vocab.txt",
"rocketqa-zh-base-query-encoder":
"https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_base_zh_vocab.txt",
"rocketqa-zh-base-para-encoder":
"https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_base_zh_vocab.txt",
"rocketqa-zh-medium-query-encoder":
"https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_medium_zh_vocab.txt",
"rocketqa-zh-medium-para-encoder":
"https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_medium_zh_vocab.txt",
"rocketqa-zh-mini-query-encoder":
"https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_mini_zh_vocab.txt",
"rocketqa-zh-mini-para-encoder":
"https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_mini_zh_vocab.txt",
"rocketqa-zh-micro-query-encoder":
"https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_micro_zh_vocab.txt",
"rocketqa-zh-micro-para-encoder":
"https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_micro_zh_vocab.txt",
"rocketqa-zh-nano-query-encoder":
"https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_nano_zh_vocab.txt",
"rocketqa-zh-nano-para-encoder":
"https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_nano_zh_vocab.txt",
"rocketqa-base-cross-encoder":
"https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_base_zh_vocab.txt",
"rocketqa-medium-cross-encoder":
"https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_medium_zh_vocab.txt",
"rocketqa-mini-cross-encoder":
"https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_mini_zh_vocab.txt",
"rocketqa-micro-cross-encoder":
"https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_micro_zh_vocab.txt",
"rocketqa-nano-cross-encoder":
"https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_nano_zh_vocab.txt",
"rocketqav2-en-marco-cross-encoder":
"https://bj.bcebos.com/paddlenlp/models/transformers/ernie_v2_base/vocab.txt",
"rocketqav2-en-marco-query-encoder":
"https://bj.bcebos.com/paddlenlp/models/transformers/ernie_v2_base/vocab.txt",
"rocketqav2-en-marco-para-encoder":
"https://bj.bcebos.com/paddlenlp/models/transformers/ernie_v2_base/vocab.txt",
"ernie-search-base-dual-encoder-marco-en":
"https://bj.bcebos.com/paddlenlp/models/transformers/ernie_v2_base/vocab.txt",
"ernie-search-large-cross-encoder-marco-en":
"https://bj.bcebos.com/paddlenlp/models/transformers/ernie_v2_large/vocab.txt",
}
}
pretrained_init_configuration = {
"ernie-1.0": {
"do_lower_case": True
},
"ernie-1.0-base-zh": {
"do_lower_case": True
},
"ernie-1.0-base-zh-cw": {
"do_lower_case": True
},
"ernie-1.0-large-zh-cw": {
"do_lower_case": True
},
"ernie-tiny": {
"do_lower_case": True
},
"ernie-2.0-base-zh": {
"do_lower_case": True
},
"ernie-2.0-large-zh": {
"do_lower_case": True
},
"ernie-2.0-base-en": {
"do_lower_case": True
},
"ernie-2.0-base-en-finetuned-squad": {
"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-en-430g": {
"do_lower_case": True
},
"rocketqa-zh-dureader-query-encoder": {
"do_lower_case": True
},
"rocketqa-zh-dureader-para-encoder": {
"do_lower_case": True
},
"rocketqa-v1-marco-query-encoder": {
"do_lower_case": True
},
"rocketqa-v1-marco-para-encoder": {
"do_lower_case": True
},
"rocketqa-zh-dureader-cross-encoder": {
"do_lower_case": True
},
"rocketqa-v1-marco-cross-encoder": {
"do_lower_case": True
},
"ernie-3.0-base-zh": {
"do_lower_case": True
},
"ernie-3.0-xbase-zh": {
"do_lower_case": True
},
"ernie-3.0-medium-zh": {
"do_lower_case": True
},
"ernie-3.0-mini-zh": {
"do_lower_case": True
},
"ernie-3.0-micro-zh": {
"do_lower_case": True
},
"ernie-3.0-nano-zh": {
"do_lower_case": True
},
"rocketqa-zh-base-query-encoder": {
"do_lower_case": True
},
"rocketqa-zh-base-para-encoder": {
"do_lower_case": True
},
"rocketqa-zh-medium-query-encoder": {
"do_lower_case": True
},
"rocketqa-zh-medium-para-encoder": {
"do_lower_case": True
},
"rocketqa-zh-mini-query-encoder": {
"do_lower_case": True
},
"rocketqa-zh-mini-para-encoder": {
"do_lower_case": True
},
"rocketqa-zh-micro-query-encoder": {
"do_lower_case": True
},
"rocketqa-zh-micro-para-encoder": {
"do_lower_case": True
},
"rocketqa-zh-nano-query-encoder": {
"do_lower_case": True
},
"rocketqa-zh-nano-para-encoder": {
"do_lower_case": True
},
"rocketqa-base-cross-encoder": {
"do_lower_case": True
},
"rocketqa-medium-cross-encoder": {
"do_lower_case": True
},
"rocketqa-mini-cross-encoder": {
"do_lower_case": True
},
"rocketqa-micro-cross-encoder": {
"do_lower_case": True
},
"rocketqa-nano-cross-encoder": {
"do_lower_case": True
},
"rocketqav2-en-marco-cross-encoder": {
"do_lower_case": True
},
"rocketqav2-en-marco-query-encoder": {
"do_lower_case": True
},
"rocketqav2-en-marco-para-encoder": {
"do_lower_case": True
},
"ernie-search-base-dual-encoder-marco-en": {
"do_lower_case": True
},
"ernie-search-large-cross-encoder-marco-en": {
"do_lower_case": True
},
}
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__(self,
vocab_file,
do_lower_case=True,
unk_token="[UNK]",
sep_token="[SEP]",
pad_token="[PAD]",
cls_token="[CLS]",
mask_token="[MASK]",
**kwargs):
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.do_lower_case = do_lower_case
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)
[文档] def extend_chinese_char(self):
"""
For, char level model such as ERNIE, we need add ## chinese token
to demonstrate the segment information.
"""
vocab_set = set(self.vocab.token_to_idx.keys())
extend_list = []
for i in range(len(self.vocab)):
if i not in self.vocab.idx_to_token:
continue
w = self.vocab.idx_to_token[i]
# Chose chinese char in [0x4E00, Ox9FA5], and try add ## char to vocab.
if len(w) == 1 and ord(w) >= 0x4E00 and ord(w) <= 0x9FA5:
new_char = "##" + w
if new_char not in vocab_set:
extend_list.append(new_char)
if len(self.vocab) + len(extend_list) > 2**16:
warnings.warn("The vocab size is larger than uint16")
new_tokens = [str(tok) for tok in extend_list]
tokens_to_add = []
for token in new_tokens:
if not isinstance(token, str):
raise TypeError(
f"Token {token} is not a string but a {type(token)}.")
if hasattr(self, "do_lower_case") and self.do_lower_case:
token = token.lower()
if (token != self.unk_token and self.convert_tokens_to_ids(token)
== self.convert_tokens_to_ids(self.unk_token)
and token not in tokens_to_add):
tokens_to_add.append(token)
if self.verbose:
print(
f"Adding {len(tokens_to_add)} ## chinese tokens to the vocabulary"
)
added_tok_encoder = dict(
(tok, len(self) + i) for i, tok in enumerate(tokens_to_add))
added_tok_decoder = {v: k for k, v in added_tok_encoder.items()}
self.added_tokens_encoder.update(added_tok_encoder)
self.added_tokens_decoder.update(added_tok_decoder)
def get_vocab(self):
return dict(self.vocab._token_to_idx, **self.added_tokens_encoder)
def _tokenize(self, text):
r"""
End-to-end tokenization for ERNIE models.
Args:
text (str): The text to be tokenized.
Returns:
List[str]: 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
[文档] def convert_tokens_to_string(self, tokens):
r"""
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[str]): A list of string representing tokens to be converted.
Returns:
str: Converted string from tokens.
Examples:
.. code-block::
from paddlenlp.transformers import ErnieTokenizer
tokenizer = ErnieTokenizer.from_pretrained('ernie-1.0')
tokens = tokenizer.tokenize('He was a puppeteer')
strings = tokenizer.convert_tokens_to_string(tokens)
#he was a puppeteer
"""
out_string = " ".join(tokens).replace(" ##", "").strip()
return out_string
[文档] def num_special_tokens_to_add(self, pair=False):
r"""
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 (bool, optional):
Whether the input is a sequence pair or a single sequence.
Defaults to `False` and the input is a single sequence.
Returns:
int: 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))
def get_special_tokens_mask(self,
token_ids_0,
token_ids_1=None,
already_has_special_tokens=False):
r"""
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):
Optional second list of IDs for sequence pairs.
Defaults to `None`.
already_has_special_tokens (str, optional):
Whether or not the token list is already formatted with special tokens for the model.
Defaults to `False`.
Returns:
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]
[文档] def build_offset_mapping_with_special_tokens(self,
offset_mapping_0,
offset_mapping_1=None):
r"""
Build offset map from a pair of offset map by concatenating and adding offsets of special tokens.
An 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 (List[tuple]):
List of char offsets to which the special tokens will be added.
offset_mapping_ids_1 (List[tuple], optional):
Optional second list of wordpiece offsets for offset mapping pairs.
Defaults to `None`.
Returns:
List[tuple]: A list of wordpiece 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)]
[文档] def create_token_type_ids_from_sequences(self,
token_ids_0,
token_ids_1=None):
r"""
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 `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).
Args:
token_ids_0 (List[int]):
A list of `inputs_ids` for the first sequence.
token_ids_1 (List[int], optional):
Optional second list of IDs for sequence pairs.
Defaults to `None`.
Returns:
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]
[文档] def get_special_tokens_mask(self,
token_ids_0,
token_ids_1=None,
already_has_special_tokens=False):
r"""
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):
Optional second list of IDs for sequence pairs.
Defaults to `None`.
already_has_special_tokens (str, optional):
Whether or not the token list is already formatted with special tokens for the model.
Defaults to `False`.
Returns:
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]
[文档] def get_vocab(self):
vocab = {
self.convert_ids_to_tokens(i): i
for i in range(self.vocab_size)
}
vocab.update(self.added_tokens_encoder)
return vocab
[文档]class ErnieTinyTokenizer(PretrainedTokenizer):
r"""
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.
Examples:
.. code-block::
from paddlenlp.transformers import ErnieTokenizer
tokenizer = ErnieTokenizer.from_pretrained('ernie-1.0')
encoded_inputs = tokenizer('He was a puppeteer')
# encoded_inputs:
# { 'input_ids': [1, 4444, 4385, 1545, 6712, 10062, 9568, 9756, 9500, 2],
# 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}
# }
Args:
vocab_file (str):
The file path of the vocabulary.
sentencepiece_model_file (str):
The file path of sentencepiece model.
word_dict(str):
The file path of word vocabulary, which is used to do chinese word segmentation.
do_lower_case (str, optional):
Whether or not to lowercase the input when tokenizing.
Defaults to`True`.
unk_token (str, optional):
A special token representing the *unknown (out-of-vocabulary)* token.
An unknown token is set to be `unk_token` inorder to be converted to an ID.
Defaults to "[UNK]".
sep_token (str, optional):
A special token separating two different sentences in the same input.
Defaults to "[SEP]".
pad_token (str, optional):
A special token used to make arrays of tokens the same size for batching purposes.
Defaults to "[PAD]".
cls_token (str, optional):
A special token used for sequence classification. It is the last token
of the sequence when built with special tokens. Defaults to "[CLS]".
mask_token (str, optional):
A special token representing a masked token. This is the token used
in the masked language modeling task which the model tries to predict the original unmasked ones.
Defaults to "[MASK]".
Examples:
.. code-block::
from paddlenlp.transformers import ErnieTinyTokenizer
tokenizer = ErnieTinyTokenizer.from_pretrained('ernie-tiny')
inputs = tokenizer('He was a puppeteer')
'''
{'input_ids': [3, 941, 977, 16690, 269, 11346, 11364, 1337, 13742, 1684, 5],
'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}
'''
"""
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://bj.bcebos.com/paddlenlp/models/transformers/ernie_tiny/vocab.txt"
},
"sentencepiece_model_file": {
"ernie-tiny":
"https://bj.bcebos.com/paddlenlp/models/transformers/ernie_tiny/spm_cased_simp_sampled.model"
},
"word_dict": {
"ernie-tiny":
"https://bj.bcebos.com/paddlenlp/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]",
**kwargs):
self.sp_model = spm.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):
r"""
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):
r"""
End-to-end tokenization for ErnieTiny models.
Args:
text (str):
The text to be tokenized.
Returns:
List(str):
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
[文档] def convert_tokens_to_string(self, tokens):
r"""
Converts a sequence of tokens (list of string) to a single string. Since
the usage of WordPiece introducing `##` to concat subwords, also removes
`##` when converting.
Args:
tokens (list): A list of string representing tokens to be converted.
Returns:
str: Converted string from tokens.
Examples:
.. code-block::
from paddlenlp.transformers import ErnieTinyTokenizer
tokenizer = ErnieTinyTokenizer.from_pretrained('ernie-tiny')
inputs = tokenizer.tokenize('He was a puppeteer')
#['▁h', '▁e', '▁was', '▁a', '▁pu', 'pp', 'e', '▁te', 'er']
strings = tokenizer.convert_tokens_to_string(tokens)
"""
out_string = " ".join(tokens).replace(" ##", "").strip()
return out_string
[文档] def save_resources(self, save_directory):
r"""
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])
if os.path.abspath(source_path) != os.path.abspath(save_path):
shutil.copyfile(source_path, save_path)
[文档] def num_special_tokens_to_add(self, pair=False):
r"""
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 (bool, optional):
Whether the input is a sequence pair or a single sequence.
Defaults to `False` and the input is a single sequence.
Returns:
int: 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))
[文档] def build_offset_mapping_with_special_tokens(self,
offset_mapping_0,
offset_mapping_1=None):
r"""
Build offset map from a pair of offset map by concatenating and adding offsets of special tokens.
An 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 (List[tuple]):
List of char offsets to which the special tokens will be added.
offset_mapping_ids_1 (List[tuple], optional):
Optional second list of wordpiece offsets for offset mapping pairs.
Defaults to `None`.
Returns:
List[tuple]: List of wordpiece 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)]
[文档] def create_token_type_ids_from_sequences(self,
token_ids_0,
token_ids_1=None):
r"""
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 `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).
Args:
token_ids_0 (List[int]):
A list of `inputs_ids` for the first sequence.
token_ids_1 (List[int], optional):
Optional second list of IDs for sequence pairs.
Defaults to `None`.
Returns:
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]
[文档] def get_special_tokens_mask(self,
token_ids_0,
token_ids_1=None,
already_has_special_tokens=False):
r"""
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):
Optional second list of IDs for sequence pairs.
Defaults to `None`.
already_has_special_tokens (str, optional):
Whether or not the token list is already formatted with special tokens for the model.
Defaults to `False`.
Returns:
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]
[文档] def get_vocab(self):
vocab = {
self.convert_ids_to_tokens(i): i
for i in range(self.vocab_size)
}
vocab.update(self.added_tokens_encoder)
return vocab