wehub-resource-sync
802 lines
30 KiB
Python
802 lines
30 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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# Copyright 2018 Google AI, Google Brain 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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"""Tokenization class for ALBERT model."""
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import os
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import unicodedata
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from shutil import copyfile
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import sentencepiece as spm
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from .. import AddedToken, BertTokenizer, PretrainedTokenizer
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__all__ = ["AlbertTokenizer", "AlbertChineseTokenizer", "AlbertEnglishTokenizer"]
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SPIECE_UNDERLINE = "▁"
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PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
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"albert-base-v1": 512,
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"albert-large-v1": 512,
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"albert-xlarge-v1": 512,
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"albert-xxlarge-v1": 512,
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"albert-base-v2": 512,
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"albert-large-v2": 512,
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"albert-xlarge-v2": 512,
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"albert-xxlarge-v2": 512,
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"albert-chinese-tiny": 512,
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"albert-chinese-small": 512,
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"albert-chinese-base": 512,
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"albert-chinese-large": 512,
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"albert-chinese-xlarge": 512,
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"albert-chinese-xxlarge": 512,
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}
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class AlbertTokenizer(PretrainedTokenizer):
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"""
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Constructs an Albert tokenizer based on SentencePiece or `BertTokenizer`.
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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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sentence_model_file (str):
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The vocabulary file (ends with '.spm') required to instantiate
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a `SentencePiece <https://github.com/google/sentencepiece>`__ tokenizer.
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do_lower_case (bool):
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Whether or not to lowercase the input when tokenizing. Defaults to `True`.
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remove_space (bool):
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Whether or note to remove space when tokenizing. Defaults to `True`.
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keep_accents (bool):
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Whether or note to keep accents when tokenizing. Defaults to `False`.
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unk_token (str):
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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):
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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):
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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):
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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):
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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 AlbertTokenizer
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tokenizer = AlbertTokenizer.from_pretrained('albert-base-v1')
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tokens = tokenizer('He was a puppeteer')
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'''
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{'input_ids': [2, 24, 23, 21, 10956, 7911, 3],
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'token_type_ids': [0, 0, 0, 0, 0, 0, 0]}
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'''
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"""
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resource_files_names = {
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"sentencepiece_model_file": "spiece.model",
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"vocab_file": "vocab.txt",
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}
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pretrained_resource_files_map = {
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"sentencepiece_model_file": {
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"albert-base-v1": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-base-v1.spiece.model",
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"albert-large-v1": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-large-v1.spiece.model",
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"albert-xlarge-v1": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-xlarge-v1.spiece.model",
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"albert-xxlarge-v1": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-xxlarge-v1.spiece.model",
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"albert-base-v2": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-base-v2.spiece.model",
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"albert-large-v2": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-large-v2.spiece.model",
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"albert-xlarge-v2": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-xlarge-v2.spiece.model",
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"albert-xxlarge-v2": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-xxlarge-v2.spiece.model",
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"albert-chinese-tiny": None,
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"albert-chinese-small": None,
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"albert-chinese-base": None,
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"albert-chinese-large": None,
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"albert-chinese-xlarge": None,
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"albert-chinese-xxlarge": None,
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},
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"vocab_file": {
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"albert-base-v1": None,
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"albert-large-v1": None,
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"albert-xlarge-v1": None,
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"albert-xxlarge-v1": None,
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"albert-base-v2": None,
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"albert-large-v2": None,
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"albert-xlarge-v2": None,
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"albert-xxlarge-v2": None,
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"albert-chinese-tiny": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-chinese-tiny.vocab.txt",
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"albert-chinese-small": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-chinese-small.vocab.txt",
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"albert-chinese-base": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-chinese-base.vocab.txt",
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"albert-chinese-large": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-chinese-large.vocab.txt",
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"albert-chinese-xlarge": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-chinese-xlarge.vocab.txt",
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"albert-chinese-xxlarge": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-chinese-xxlarge.vocab.txt",
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},
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}
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pretrained_init_configuration = {
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"albert-base-v1": {
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"do_lower_case": True,
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"remove_space": True,
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"keep_accents": False,
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"unk_token": "<unk>",
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"pad_token": "<pad>",
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},
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"albert-large-v1": {
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"do_lower_case": True,
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"remove_space": True,
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"keep_accents": False,
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"unk_token": "<unk>",
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"pad_token": "<pad>",
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},
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"albert-xlarge-v1": {
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"do_lower_case": True,
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"remove_space": True,
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"keep_accents": False,
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"unk_token": "<unk>",
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"pad_token": "<pad>",
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},
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"albert-xxlarge-v1": {
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"do_lower_case": True,
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"remove_space": True,
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"keep_accents": False,
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"unk_token": "<unk>",
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"pad_token": "<pad>",
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},
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"albert-base-v2": {
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"do_lower_case": True,
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"remove_space": True,
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"keep_accents": False,
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"unk_token": "<unk>",
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"pad_token": "<pad>",
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},
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"albert-large-v2": {
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"do_lower_case": True,
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"remove_space": True,
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"keep_accents": False,
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"unk_token": "<unk>",
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"pad_token": "<pad>",
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},
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"albert-xlarge-v2": {
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"do_lower_case": True,
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"remove_space": True,
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"keep_accents": False,
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"unk_token": "<unk>",
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"pad_token": "<pad>",
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},
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"albert-xxlarge-v2": {
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"do_lower_case": True,
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"remove_space": True,
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"keep_accents": False,
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"unk_token": "<unk>",
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"pad_token": "<pad>",
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},
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"albert-chinese-tiny": {
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"do_lower_case": False,
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"unk_token": "[UNK]",
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"pad_token": "[PAD]",
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},
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"albert-chinese-small": {
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"do_lower_case": False,
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"unk_token": "[UNK]",
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"pad_token": "[PAD]",
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},
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"albert-chinese-base": {
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"do_lower_case": False,
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"unk_token": "[UNK]",
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"pad_token": "[PAD]",
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},
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"albert-chinese-large": {
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"do_lower_case": False,
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"unk_token": "[UNK]",
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"pad_token": "[PAD]",
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},
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"albert-chinese-xlarge": {
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"do_lower_case": False,
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"unk_token": "[UNK]",
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"pad_token": "[PAD]",
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},
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"albert-chinese-xxlarge": {
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"do_lower_case": False,
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"unk_token": "[UNK]",
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"pad_token": "[PAD]",
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},
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}
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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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sentencepiece_model_file,
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do_lower_case=True,
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remove_space=True,
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keep_accents=False,
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bos_token="[CLS]",
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eos_token="[SEP]",
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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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mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
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self._build_special_tokens_map_extended(mask_token=mask_token)
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self.do_lower_case = do_lower_case
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self.remove_space = remove_space
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self.keep_accents = keep_accents
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self.vocab_file = vocab_file
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self.sentencepiece_model_file = sentencepiece_model_file
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if vocab_file is not None:
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self.tokenizer = AlbertChineseTokenizer(
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vocab_file=vocab_file,
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do_lower_case=do_lower_case,
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unk_token=unk_token,
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sep_token=sep_token,
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pad_token=pad_token,
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cls_token=cls_token,
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mask_token=mask_token,
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**kwargs,
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)
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elif sentencepiece_model_file is not None:
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self.tokenizer = AlbertEnglishTokenizer(
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sentencepiece_model_file=sentencepiece_model_file,
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do_lower_case=do_lower_case,
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remove_space=remove_space,
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keep_accents=keep_accents,
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bos_token=bos_token,
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eos_token=eos_token,
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unk_token=unk_token,
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sep_token=sep_token,
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pad_token=pad_token,
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cls_token=cls_token,
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mask_token=mask_token,
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**kwargs,
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)
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else:
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raise ValueError(
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"You should only specify either one(not both) of 'vocal_file'"
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"and 'sentencepiece_model_file' to construct an albert tokenizer."
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"Specify 'vocal_file' for Chinese tokenizer and "
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"'sentencepiece_model_file' for English tokenizer"
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)
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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 self.tokenizer.vocab_size
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def _tokenize(self, text):
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return self.tokenizer._tokenize(text)
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def tokenize(self, text):
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"""
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Converts a string to a list of tokens.
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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(str): A list of string representing converted tokens.
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Examples:
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.. code-block::
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from paddlenlp.transformers import RobertaTokenizer
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tokenizer = RobertaTokenizer.from_pretrained('roberta-wwm-ext')
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tokens = tokenizer.tokenize('He was a puppeteer')
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"""
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return self.tokenizer.tokenize(text)
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def _convert_token_to_id(self, token):
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"""
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Converts a sequence of tokens (list of string) to a list of ids.
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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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list: Converted ids from tokens.
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Examples:
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.. code-block::
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from paddlenlp.transformers import AlbertTokenizer
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tokenizer = AlbertTokenizer.from_pretrained('bert-base-uncased')
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tokens = tokenizer.tokenize('He was a puppeteer')
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#['▁he', '▁was', '▁a', '▁puppet', 'eer']
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ids = tokenizer.convert_tokens_to_ids(tokens)
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#[24, 23, 21, 10956, 7911]
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"""
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return self.tokenizer._convert_token_to_id(token)
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def _convert_id_to_token(self, index):
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"""
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Converts a sequence of tokens (list of string) to a list of ids.
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Args:
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ids (list): A list of ids to be converted.
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skip_special_tokens (bool, optional):
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Whether or not to skip special tokens. Defaults to `False`.
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Returns:
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list: A list of converted tokens.
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Examples:
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.. code-block::
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from paddlenlp.transformers import AlbertTokenizer
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tokenizer = AlbertTokenizer.from_pretrained('bert-base-uncased')
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ids = [24, 23, 21, 10956, 7911]
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tokens = tokenizer.convert_ids_to_tokens(ids)
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#['▁he', '▁was', '▁a', '▁puppet', 'eer']
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"""
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return self.tokenizer._convert_id_to_token(index)
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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.
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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 AlbertTokenizer
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tokenizer = AlbertTokenizer.from_pretrained('bert-base-uncased')
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tokens = tokenizer.tokenize('He was a puppeteer')
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'''
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['▁he', '▁was', '▁a', '▁puppet', 'eer']
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'''
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strings = tokenizer.convert_tokens_to_string(tokens)
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'''
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he was a puppeteer
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'''
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"""
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return self.tokenizer.convert_tokens_to_string(tokens)
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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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return self.tokenizer.num_special_tokens_to_add(pair=pair)
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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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An Albert 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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return self.tokenizer.build_inputs_with_special_tokens(token_ids_0, token_ids_1=token_ids_1)
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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 Albert 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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return self.tokenizer.build_offset_mapping_with_special_tokens(
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offset_mapping_0, offset_mapping_1=offset_mapping_1
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)
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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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return self.tokenizer.get_special_tokens_mask(
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token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=already_has_special_tokens
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)
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def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None):
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return self.tokenizer.create_token_type_ids_from_sequences(token_ids_0, token_ids_1=token_ids_1)
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def save_resources(self, save_directory):
|
|
return self.tokenizer.save_resources(save_directory)
|
|
|
|
|
|
class AlbertEnglishTokenizer(PretrainedTokenizer):
|
|
resource_files_names = {
|
|
"sentencepiece_model_file": "spiece.model",
|
|
}
|
|
|
|
pretrained_resource_files_map = {
|
|
"sentencepiece_model_file": {
|
|
"albert-base-v1": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-base-v1.spiece.model",
|
|
"albert-large-v1": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-large-v1.spiece.model",
|
|
"albert-xlarge-v1": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-xlarge-v1.spiece.model",
|
|
"albert-xxlarge-v1": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-xxlarge-v1.spiece.model",
|
|
"albert-base-v2": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-base-v2.spiece.model",
|
|
"albert-large-v2": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-large-v2.spiece.model",
|
|
"albert-xlarge-v2": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-xlarge-v2.spiece.model",
|
|
"albert-xxlarge-v2": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-xxlarge-v2.spiece.model",
|
|
},
|
|
}
|
|
|
|
pretrained_init_configuration = {
|
|
"albert-base-v1": {
|
|
"do_lower_case": True,
|
|
"remove_space": True,
|
|
"keep_accents": False,
|
|
"unk_token": "<unk>",
|
|
"pad_token": "<pad>",
|
|
},
|
|
"albert-large-v1": {
|
|
"do_lower_case": True,
|
|
"remove_space": True,
|
|
"keep_accents": False,
|
|
"unk_token": "<unk>",
|
|
"pad_token": "<pad>",
|
|
},
|
|
"albert-xlarge-v1": {
|
|
"do_lower_case": True,
|
|
"remove_space": True,
|
|
"keep_accents": False,
|
|
"unk_token": "<unk>",
|
|
"pad_token": "<pad>",
|
|
},
|
|
"albert-xxlarge-v1": {
|
|
"do_lower_case": True,
|
|
"remove_space": True,
|
|
"keep_accents": False,
|
|
"unk_token": "<unk>",
|
|
"pad_token": "<pad>",
|
|
},
|
|
"albert-base-v2": {
|
|
"do_lower_case": True,
|
|
"remove_space": True,
|
|
"keep_accents": False,
|
|
"unk_token": "<unk>",
|
|
"pad_token": "<pad>",
|
|
},
|
|
"albert-large-v2": {
|
|
"do_lower_case": True,
|
|
"remove_space": True,
|
|
"keep_accents": False,
|
|
"unk_token": "<unk>",
|
|
"pad_token": "<pad>",
|
|
},
|
|
"albert-xlarge-v2": {
|
|
"do_lower_case": True,
|
|
"remove_space": True,
|
|
"keep_accents": False,
|
|
"unk_token": "<unk>",
|
|
"pad_token": "<pad>",
|
|
},
|
|
"albert-xxlarge-v2": {
|
|
"do_lower_case": True,
|
|
"remove_space": True,
|
|
"keep_accents": False,
|
|
"unk_token": "<unk>",
|
|
"pad_token": "<pad>",
|
|
},
|
|
}
|
|
max_model_input_sizes = {
|
|
"albert-base-v1": 512,
|
|
"albert-large-v1": 512,
|
|
"albert-xlarge-v1": 512,
|
|
"albert-xxlarge-v1": 512,
|
|
"albert-base-v2": 512,
|
|
"albert-large-v2": 512,
|
|
"albert-xlarge-v2": 512,
|
|
"albert-xxlarge-v2": 512,
|
|
}
|
|
|
|
def __init__(
|
|
self,
|
|
sentencepiece_model_file,
|
|
do_lower_case=True,
|
|
remove_space=True,
|
|
keep_accents=False,
|
|
bos_token="[CLS]",
|
|
eos_token="[SEP]",
|
|
unk_token="<unk>",
|
|
sep_token="[SEP]",
|
|
pad_token="<pad>",
|
|
cls_token="[CLS]",
|
|
mask_token="[MASK]",
|
|
sp_model_kwargs=None,
|
|
**kwargs
|
|
):
|
|
|
|
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
|
|
self.do_lower_case = do_lower_case
|
|
self.remove_space = remove_space
|
|
self.keep_accents = keep_accents
|
|
self.sentencepiece_model_file = sentencepiece_model_file
|
|
|
|
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
|
|
self.sp_model.Load(sentencepiece_model_file)
|
|
|
|
@property
|
|
def vocab_size(self):
|
|
return len(self.sp_model)
|
|
|
|
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
|
|
|
|
def __getstate__(self):
|
|
state = self.__dict__.copy()
|
|
state["sp_model"] = None
|
|
return state
|
|
|
|
def __setstate__(self, d):
|
|
self.__dict__ = d
|
|
if not hasattr(self, "sp_model_kwargs"):
|
|
self.sp_model_kwargs = {}
|
|
|
|
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
|
|
self.sp_model.Load(self.sentencepiece_model_file)
|
|
|
|
def preprocess_text(self, inputs):
|
|
if self.remove_space:
|
|
outputs = " ".join(inputs.strip().split())
|
|
else:
|
|
outputs = inputs
|
|
outputs = outputs.replace("``", '"').replace("''", '"')
|
|
|
|
if not self.keep_accents:
|
|
outputs = unicodedata.normalize("NFKD", outputs)
|
|
outputs = "".join([c for c in outputs if not unicodedata.combining(c)])
|
|
if self.do_lower_case:
|
|
outputs = outputs.lower()
|
|
|
|
return outputs
|
|
|
|
def _tokenize(self, text):
|
|
"""Tokenize a string."""
|
|
text = self.preprocess_text(text)
|
|
pieces = self.sp_model.encode(text, out_type=str)
|
|
new_pieces = []
|
|
for piece in pieces:
|
|
if len(piece) > 1 and piece[-1] == str(",") and piece[-2].isdigit():
|
|
cur_pieces = self.sp_model.EncodeAsPieces(piece[:-1].replace(SPIECE_UNDERLINE, ""))
|
|
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
|
|
if len(cur_pieces[0]) == 1:
|
|
cur_pieces = cur_pieces[1:]
|
|
else:
|
|
cur_pieces[0] = cur_pieces[0][1:]
|
|
cur_pieces.append(piece[-1])
|
|
new_pieces.extend(cur_pieces)
|
|
else:
|
|
new_pieces.append(piece)
|
|
|
|
return new_pieces
|
|
|
|
def _convert_token_to_id(self, token):
|
|
"""Converts a token (str) to an id using the vocab."""
|
|
return self.sp_model.PieceToId(token)
|
|
|
|
def _convert_id_to_token(self, index):
|
|
"""Converts an index (integer) to a token (str) using the vocab."""
|
|
return self.sp_model.IdToPiece(index)
|
|
|
|
def convert_tokens_to_string(self, tokens):
|
|
"""Converts a sequence of tokens (strings for sub-words) in a single string."""
|
|
out_string = "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip()
|
|
return out_string
|
|
|
|
def num_special_tokens_to_add(self, pair=False):
|
|
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 build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
|
|
sep = [self.sep_token_id]
|
|
cls = [self.cls_token_id]
|
|
if token_ids_1 is None:
|
|
return cls + token_ids_0 + sep
|
|
return cls + token_ids_0 + sep + token_ids_1 + sep
|
|
|
|
def build_offset_mapping_with_special_tokens(self, offset_mapping_0, offset_mapping_1=None):
|
|
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)]
|
|
|
|
def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False):
|
|
|
|
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]
|
|
|
|
def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None):
|
|
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]
|
|
|
|
def save_resources(self, save_directory):
|
|
for name, file_name in self.resource_files_names.items():
|
|
save_path = os.path.join(save_directory, file_name)
|
|
if os.path.abspath(self.sentencepiece_model_file) != os.path.abspath(save_path) and os.path.isfile(
|
|
self.sentencepiece_model_file
|
|
):
|
|
copyfile(self.sentencepiece_model_file, save_path)
|
|
elif not os.path.isfile(self.sentencepiece_model_file):
|
|
with open(save_path, "wb") as fi:
|
|
content_spiece_model = self.sp_model.serialized_model_proto()
|
|
fi.write(content_spiece_model)
|
|
|
|
|
|
class AlbertChineseTokenizer(BertTokenizer):
|
|
resource_files_names = {"vocab_file": "vocab.txt"}
|
|
pretrained_resource_files_map = {
|
|
"vocab_file": {
|
|
"albert-chinese-tiny": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-chinese-tiny.vocab.txt",
|
|
"albert-chinese-small": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-chinese-small.vocab.txt",
|
|
"albert-chinese-base": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-chinese-base.vocab.txt",
|
|
"albert-chinese-large": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-chinese-large.vocab.txt",
|
|
"albert-chinese-xlarge": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-chinese-xlarge.vocab.txt",
|
|
"albert-chinese-xxlarge": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-chinese-xxlarge.vocab.txt",
|
|
}
|
|
}
|
|
pretrained_init_configuration = {
|
|
"albert-chinese-tiny": {
|
|
"do_lower_case": False,
|
|
"unk_token": "[UNK]",
|
|
"pad_token": "[PAD]",
|
|
},
|
|
"albert-chinese-small": {
|
|
"do_lower_case": False,
|
|
"unk_token": "[UNK]",
|
|
"pad_token": "[PAD]",
|
|
},
|
|
"albert-chinese-base": {
|
|
"do_lower_case": False,
|
|
"unk_token": "[UNK]",
|
|
"pad_token": "[PAD]",
|
|
},
|
|
"albert-chinese-large": {
|
|
"do_lower_case": False,
|
|
"unk_token": "[UNK]",
|
|
"pad_token": "[PAD]",
|
|
},
|
|
"albert-chinese-xlarge": {
|
|
"do_lower_case": False,
|
|
"unk_token": "[UNK]",
|
|
"pad_token": "[PAD]",
|
|
},
|
|
"albert-chinese-xxlarge": {
|
|
"do_lower_case": False,
|
|
"unk_token": "[UNK]",
|
|
"pad_token": "[PAD]",
|
|
},
|
|
}
|
|
max_model_input_sizes = {
|
|
"albert-chinese-tiny": 512,
|
|
"albert-chinese-small": 512,
|
|
"albert-chinese-base": 512,
|
|
"albert-chinese-large": 512,
|
|
"albert-chinese-xlarge": 512,
|
|
"albert-chinese-xxlarge": 512,
|
|
}
|
|
|
|
def __init__(
|
|
self,
|
|
vocab_file,
|
|
do_lower_case=True,
|
|
do_basic_tokenize=True,
|
|
never_split=None,
|
|
unk_token="[UNK]",
|
|
sep_token="[SEP]",
|
|
pad_token="[PAD]",
|
|
cls_token="[CLS]",
|
|
mask_token="[MASK]",
|
|
tokenize_chinese_chars=True,
|
|
strip_accents=None,
|
|
**kwargs
|
|
):
|
|
super(AlbertChineseTokenizer, self).__init__(
|
|
vocab_file,
|
|
do_lower_case=do_lower_case,
|
|
do_basic_tokenize=do_basic_tokenize,
|
|
never_split=never_split,
|
|
unk_token=unk_token,
|
|
sep_token=sep_token,
|
|
pad_token=pad_token,
|
|
cls_token=cls_token,
|
|
mask_token=mask_token,
|
|
tokenize_chinese_chars=tokenize_chinese_chars,
|
|
strip_accents=strip_accents,
|
|
**kwargs,
|
|
)
|