wehub-resource-sync
103 lines
3.8 KiB
Python
103 lines
3.8 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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# Copyright 2018 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 classes for MegatronBert."""
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from .. import BertTokenizer
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__all__ = ["MegatronBertTokenizer"]
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PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"megatronbert-cased": 512, "megatronbert-uncased": 512}
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class MegatronBertTokenizer(BertTokenizer):
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"""
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Constructs a MegatronBert 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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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):
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Whether or not to lowercase the input when tokenizing.
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Defaults to`True`.
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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 MegatronBertTokenizer
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tokenizer = MegatronBertTokenizer.from_pretrained('MegatronBert-uncased')
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inputs = tokenizer('He was a puppeteer')
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print(inputs)
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'''
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{'input_ids': [101, 2002, 2001, 1037, 13997, 11510, 102], '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 = {"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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"megatronbert-uncased": "https://bj.bcebos.com/paddle-hapi/models/bert/bert-base-uncased-vocab.txt",
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"megatronbert-cased": "https://bj.bcebos.com/paddle-hapi/models/bert/bert-base-cased-vocab.txt",
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}
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}
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pretrained_init_configuration = {
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"megatronbert-uncased": {"do_lower_case": True},
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"megatronbert-cased": {"do_lower_case": False},
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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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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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super(MegatronBertTokenizer, self).__init__(
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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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