wehub-resource-sync
307 lines
12 KiB
Python
307 lines
12 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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from ..bert.tokenizer import BasicTokenizer, WordpieceTokenizer
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from ..tokenizer_utils import PretrainedTokenizer
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__all__ = ["RoFormerv2Tokenizer"]
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PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
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"roformer_v2_chinese_char_small": 512,
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"roformer_v2_chinese_char_base": 512,
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"roformer_v2_chinese_char_large": 512,
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}
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class RoFormerv2Tokenizer(PretrainedTokenizer):
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"""
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Constructs a RoFormerv2 tokenizer. It uses a basic tokenizer to do punctuation
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splitting, lower casing and so on, and follows a WordPiece tokenizer to
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tokenize as subwords.
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This tokenizer inherits from :class:`~paddlenlp.transformers.tokenizer_utils.PretrainedTokenizer`
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which contains most of the main methods. For more information regarding those methods,
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please refer to this superclass.
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Args:
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vocab_file (str):
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The vocabulary file path (ends with '.txt') required to instantiate
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a `WordpieceTokenizer`.
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do_lower_case (bool,optional):
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Whether or not to lowercase the input when tokenizing.
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If you use the RoFormerv2 pretrained model, lower is set to
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False when using the cased model, otherwise it is set to True.
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Defaults to`True`.
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unk_token (str,optional):
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A special token representing the *unknown (out-of-vocabulary)* token.
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An unknown token is set to be `unk_token` inorder to be converted to an ID.
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Defaults to "[UNK]".
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sep_token (str,optional):
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A special token separating two different sentences in the same input.
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Defaults to "[SEP]".
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pad_token (str,optional):
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A special token used to make arrays of tokens the same size for batching purposes.
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Defaults to "[PAD]".
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cls_token (str,optional):
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A special token used for sequence classification. It is the last token
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of the sequence when built with special tokens. Defaults to "[CLS]".
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mask_token (str,optional):
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A special token representing a masked token. This is the token used
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in the masked language modeling task which the model tries to predict the original unmasked ones.
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Defaults to "[MASK]".
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Examples:
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.. code-block::
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from paddlenlp.transformers import RoFormerv2Tokenizer
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tokenizer = RoFormerv2Tokenizer.from_pretrained('roformer_v2_chinese_char_base')
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tokens = tokenizer('欢迎使用百度飞桨')
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'''
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{'input_ids': [101, 3223, 6500, 421, 4179, 4331, 2008, 7263, 3055, 102],
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'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}
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'''
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"""
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resource_files_names = {"vocab_file": "vocab.txt"} # for save_pretrained
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pretrained_resource_files_map = {
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"vocab_file": {
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"roformer_v2_chinese_char_small": "https://bj.bcebos.com/paddlenlp/models/transformers/roformerv2/roformer_v2_chinese_char_small/vocab.txt",
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"roformer_v2_chinese_char_base": "https://bj.bcebos.com/paddlenlp/models/transformers/roformerv2/roformer_v2_chinese_char_base/vocab.txt",
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"roformer_v2_chinese_char_large": "https://bj.bcebos.com/paddlenlp/models/transformers/roformerv2/roformer_v2_chinese_char_large/vocab.txt",
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}
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}
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pretrained_init_configuration = {
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"roformer_v2_chinese_char_small": {"do_lower_case": True},
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"roformer_v2_chinese_char_base": {"do_lower_case": True},
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"roformer_v2_chinese_char_large": {"do_lower_case": True},
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}
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# TODO(wj-Mcat): to be confirmed
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max_model_input_sizes = {
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"roformer_v2_chinese_char_small": 1024,
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"roformer_v2_chinese_char_base": 1024,
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"roformer_v2_chinese_char_large": 1024,
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}
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padding_side = "right"
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max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
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def __init__(
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self,
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vocab_file,
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do_lower_case=True,
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unk_token="[UNK]",
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sep_token="[SEP]",
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pad_token="[PAD]",
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cls_token="[CLS]",
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mask_token="[MASK]",
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**kwargs
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):
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if not os.path.isfile(vocab_file):
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raise ValueError(
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"Can't find a vocabulary file at path '{}'. To load the "
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"vocabulary from a pretrained model please use "
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"`tokenizer = RoFormerv2Tokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`".format(vocab_file)
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)
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self.do_lower_case = do_lower_case
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self.vocab = self.load_vocabulary(vocab_file, unk_token=unk_token)
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self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case)
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self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=unk_token)
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@property
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def vocab_size(self):
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"""
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Return the size of vocabulary.
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Returns:
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int: The size of vocabulary.
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"""
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return len(self.vocab)
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def _tokenize(self, text):
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"""
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End-to-end tokenization for RoFormerv2 models.
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Args:
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text (str): The text to be tokenized.
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Returns:
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list: A list of string representing converted tokens.
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"""
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split_tokens = []
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for token in self.basic_tokenizer.tokenize(text):
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for sub_token in self.wordpiece_tokenizer.tokenize(token):
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split_tokens.append(sub_token)
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return split_tokens
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def convert_tokens_to_string(self, tokens):
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"""
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Converts a sequence of tokens (list of string) to a single string. Since
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the usage of WordPiece introducing `##` to concat subwords, also removes
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`##` when converting.
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Args:
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tokens (list): A list of string representing tokens to be converted.
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Returns:
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str: Converted string from tokens.
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Examples:
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.. code-block::
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from paddlenlp.transformers import RoFormerv2Tokenizer
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tokenizer = RoFormerv2Tokenizer.from_pretrained('roformer_v2_chinese_char_base')
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tokens = tokenizer.tokenize('欢迎使用百度飞桨!')
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'''
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['欢', '迎', '使', '用', '百', '度', '飞', '桨', '!']
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'''
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strings = tokenizer.convert_tokens_to_string(tokens)
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'''
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'欢 迎 使 用 百 度 飞 桨 !'
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'''
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"""
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out_string = " ".join(tokens).replace(" ##", "").strip()
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return out_string
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def num_special_tokens_to_add(self, pair=False):
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"""
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Returns the number of added tokens when encoding a sequence with special tokens.
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Args:
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pair(bool):
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Whether the input is a sequence pair or a single sequence.
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Defaults to `False` and the input is a single sequence.
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Returns:
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int: Number of tokens added to sequences.
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"""
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token_ids_0 = []
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token_ids_1 = []
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return len(self.build_inputs_with_special_tokens(token_ids_0, token_ids_1 if pair else None))
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def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
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"""
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Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
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adding special tokens.
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A RoFormerv2 sequence has the following format:
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- single sequence: ``[CLS] X [SEP]``
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- pair of sequences: ``[CLS] A [SEP] B [SEP]``
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Args:
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token_ids_0 (List[int]):
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List of IDs to which the special tokens will be added.
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token_ids_1 (List[int], optional):
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Optional second list of IDs for sequence pairs. Defaults to None.
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Returns:
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List[int]: List of input_id with the appropriate special tokens.
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"""
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if token_ids_1 is None:
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return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
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_cls = [self.cls_token_id]
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_sep = [self.sep_token_id]
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return _cls + token_ids_0 + _sep + token_ids_1 + _sep
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def build_offset_mapping_with_special_tokens(self, offset_mapping_0, offset_mapping_1=None):
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"""
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Build offset map from a pair of offset map by concatenating and adding offsets of special tokens.
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A RoFormerv2 offset_mapping has the following format:
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- single sequence: ``(0,0) X (0,0)``
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- pair of sequences: ``(0,0) A (0,0) B (0,0)``
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Args:
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offset_mapping_ids_0 (List[tuple]):
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List of wordpiece offsets to which the special tokens will be added.
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offset_mapping_ids_1 (List[tuple], optional):
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Optional second list of wordpiece offsets for offset mapping pairs. Defaults to None.
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Returns:
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List[tuple]: A list of wordpiece offsets with the appropriate offsets of special tokens.
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"""
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if offset_mapping_1 is None:
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return [(0, 0)] + offset_mapping_0 + [(0, 0)]
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return [(0, 0)] + offset_mapping_0 + [(0, 0)] + offset_mapping_1 + [(0, 0)]
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def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None):
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"""
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Create a mask from the two sequences passed to be used in a sequence-pair classification task.
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A RoFormerv2 sequence pair mask has the following format:
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::
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0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
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| first sequence | second sequence |
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If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).
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Args:
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token_ids_0 (List[int]):
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A list of `inputs_ids` for the first sequence.
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token_ids_1 (List[int], optional):
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Optional second list of IDs for sequence pairs. Defaults to None.
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Returns:
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List[int]: List of token_type_id according to the given sequence(s).
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"""
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_sep = [self.sep_token_id]
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_cls = [self.cls_token_id]
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if token_ids_1 is None:
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return len(_cls + token_ids_0 + _sep) * [0]
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return len(_cls + token_ids_0 + _sep) * [0] + len(token_ids_1 + _sep) * [1]
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def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False):
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"""
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Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
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special tokens using the tokenizer ``encode`` methods.
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Args:
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token_ids_0 (List[int]):
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A list of `inputs_ids` for the first sequence.
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token_ids_1 (List[int], optional):
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Optional second list of IDs for sequence pairs. Defaults to None.
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already_has_special_tokens (bool, optional): Whether or not the token list is already
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formatted with special tokens for the model. Defaults to None.
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Returns:
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List[int]: The list of integers either be 0 or 1: 1 for a special token, 0 for a sequence token.
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"""
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if already_has_special_tokens:
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if token_ids_1 is not None:
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raise ValueError(
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"You should not supply a second sequence if the provided sequence of "
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"ids is already formatted with special tokens for the model."
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)
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return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0))
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if token_ids_1 is not None:
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return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
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return [1] + ([0] * len(token_ids_0)) + [1]
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def get_vocab(self):
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return dict(self.vocab._token_to_idx, **self.added_tokens_encoder)
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