wehub-resource-sync
209 lines
9.3 KiB
Python
209 lines
9.3 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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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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# http://www.apache.org/licenses/LICENSE-2.0
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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 FNet model."""
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from typing import Any, Dict, List, Optional
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import sentencepiece as spm
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from ..albert.tokenizer import AddedToken, AlbertEnglishTokenizer
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__all__ = ["FNetTokenizer"]
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SPIECE_UNDERLINE = "▁"
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PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"fnet-base": 512, "fnet-large": 512}
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class FNetTokenizer(AlbertEnglishTokenizer):
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"""
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Construct a FNet tokenizer. Inherit from :class:`AlbertEnglishTokenizer`. Based on `SentencePiece
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<https://github.com/google/sentencepiece>`__.
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Args:
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sentencepiece_model_file (:obj:`str`):
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`SentencePiece <https://github.com/google/sentencepiece>`__ file (generally has a `.spm` extension) that
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contains the vocabulary necessary to instantiate a tokenizer.
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do_lower_case (:obj:`bool`, `optional`, defaults to :obj:`False`):
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Whether or not to lowercase the input when tokenizing.
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remove_space (:obj:`bool`, `optional`, defaults to :obj:`True`):
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Whether or not to strip the text when tokenizing (removing excess spaces before and after the string).
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keep_accents (:obj:`bool`, `optional`, defaults to :obj:`True`):
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Whether or not to keep accents when tokenizing.
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unk_token (:obj:`str`, `optional`, defaults to :obj:`"<unk>"`):
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The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
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token instead.
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sep_token (:obj:`str`, `optional`, defaults to :obj:`"[SEP]"`):
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The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
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sequence classification or for a text and a question for question answering. It is also used as the last
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token of a sequence built with special tokens.
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pad_token (:obj:`str`, `optional`, defaults to :obj:`"<pad>"`):
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The token used for padding, for example when batching sequences of different lengths.
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cls_token (:obj:`str`, `optional`, defaults to :obj:`"[CLS]"`):
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The classifier token which is used when doing sequence classification (classification of the whole sequence
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instead of per-token classification). It is the first token of the sequence when built with special tokens.
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mask_token (:obj:`str`, `optional`, defaults to :obj:`"[MASK]"`):
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The token used for masking values. This is the token used when training this model with masked language
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modeling. This is the token which the model will try to predict.
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sp_model_kwargs (:obj:`dict`, `optional`):
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Will be passed to the ``SentencePieceProcessor.__init__()`` method. The `Python wrapper for SentencePiece
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<https://github.com/google/sentencepiece/tree/master/python>`__ can be used, among other things, to set:
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- ``enable_sampling``: Enable subword regularization.
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- ``nbest_size``: Sampling parameters for unigram. Invalid for BPE-Dropout.
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- ``nbest_size = {0,1}``: No sampling is performed.
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- ``nbest_size > 1``: samples from the nbest_size results.
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- ``nbest_size < 0``: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
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using forward-filtering-and-backward-sampling algorithm.
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- ``alpha``: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
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BPE-dropout.
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Attributes:
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sp_model (:obj:`SentencePieceProcessor`):
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The `SentencePiece` processor that is used for every conversion (string, tokens and IDs).
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"""
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resource_files_names = {
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"sentencepiece_model_file": "spiece.model",
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}
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pretrained_resource_files_map = {
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"sentencepiece_model_file": {
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"fnet-base": "https://bj.bcebos.com/paddlenlp/models/transformers/fnet/fnet-base/spiece.model",
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"fnet-large": "https://bj.bcebos.com/paddlenlp/models/transformers/fnet/fnet-large/spiece.model",
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}
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}
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pretrained_init_configuration = {
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"fnet-base": {
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"do_lower_case": False,
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},
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"fnet-large": {
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"do_lower_case": False,
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},
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}
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model_input_names = ["input_ids", "token_type_ids"]
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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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sentencepiece_model_file,
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do_lower_case=False,
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remove_space=True,
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keep_accents=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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sp_model_kwargs: Optional[Dict[str, Any]] = None,
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**kwargs
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):
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# Mask token behave like a normal word, i.e. include the space before it
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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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super().__init__(
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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=cls_token,
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eos_token=sep_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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sp_model_kwargs=sp_model_kwargs,
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**kwargs,
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)
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self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
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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.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
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self.sp_model.Load(sentencepiece_model_file)
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def convert_tokens_to_string(self, tokens):
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"""Converts a sequence of tokens (strings for sub-words) in a single string."""
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out_string = "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip()
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return out_string
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def build_inputs_with_special_tokens(
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
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) -> List[int]:
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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. An FNet 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 (:obj:`List[int]`):
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List of IDs to which the special tokens will be added.
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token_ids_1 (:obj:`List[int]`, `optional`):
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Optional second list of IDs for sequence pairs.
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Returns:
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:obj:`List[int]`: List of `input IDs <../glossary.html#input-ids>`__ with the appropriate special tokens.
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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 cls + token_ids_0 + sep
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return cls + token_ids_0 + sep + token_ids_1 + sep
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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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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 create_token_type_ids_from_sequences(
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
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) -> List[int]:
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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. An FNet sequence
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pair mask has the following format: ::
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0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | first sequence | second sequence |
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If :obj:`token_ids_1` is :obj:`None`, this method only returns the first portion of the mask (0s).
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Args:
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token_ids_0 (:obj:`List[int]`):
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List of IDs.
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token_ids_1 (:obj:`List[int]`, `optional`):
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Optional second list of IDs for sequence pairs.
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Returns:
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:obj:`List[int]`: List of `token type IDs <../glossary.html#token-type-ids>`_ according to the given
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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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