# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Tokenization class for Funnel Transformer."""
from typing import List, Optional
from .tokenization_bert import BertTokenizer, BertTokenizerFast
from .utils import logging
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
_model_names = [
"small",
"small-base",
"medium",
"medium-base",
"intermediate",
"intermediate-base",
"large",
"large-base",
"xlarge",
"xlarge-base",
]
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"funnel-transformer/small": "https://s3.amazonaws.com/models.huggingface.co/bert/funnel-transformer/small/vocab.txt",
"funnel-transformer/small-base": "https://s3.amazonaws.com/models.huggingface.co/bert/funnel-transformer/small-base/vocab.txt",
"funnel-transformer/medium": "https://s3.amazonaws.com/models.huggingface.co/bert/funnel-transformer/medium/vocab.txt",
"funnel-transformer/medium-base": "https://s3.amazonaws.com/models.huggingface.co/bert/funnel-transformer/medium-base/vocab.txt",
"funnel-transformer/intermediate": "https://s3.amazonaws.com/models.huggingface.co/bert/funnel-transformer/intermediate/vocab.txt",
"funnel-transformer/intermediate-base": "https://s3.amazonaws.com/models.huggingface.co/bert/funnel-transformer/intermediate-base/vocab.txt",
"funnel-transformer/large": "https://s3.amazonaws.com/models.huggingface.co/bert/funnel-transformer/large/vocab.txt",
"funnel-transformer/large-base": "https://s3.amazonaws.com/models.huggingface.co/bert/funnel-transformer/large-base/vocab.txt",
"funnel-transformer/xlarge": "https://s3.amazonaws.com/models.huggingface.co/bert/funnel-transformer/xlarge/vocab.txt",
"funnel-transformer/xlarge-base": "https://s3.amazonaws.com/models.huggingface.co/bert/funnel-transformer/xlarge-base/vocab.txt",
}
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {f"funnel-transformer/{name}": 512 for name in _model_names}
PRETRAINED_INIT_CONFIGURATION = {f"funnel-transformer/{name}": {"do_lower_case": True} for name in _model_names}
[docs]class FunnelTokenizer(BertTokenizer):
r"""
Tokenizer for the Funnel Transformer models.
:class:`~transformers.FunnelTokenizer` is identical to :class:`~transformers.BertTokenizer` and runs end-to-end
tokenization: punctuation splitting + wordpiece.
Refer to superclass :class:`~transformers.BertTokenizer` for usage examples and documentation concerning
parameters.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
cls_token_type_id: int = 2
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>",
bos_token="<s>",
eos_token="</s>",
tokenize_chinese_chars=True,
strip_accents=None,
**kwargs
):
super().__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,
bos_token=bos_token,
eos_token=eos_token,
tokenize_chinese_chars=tokenize_chinese_chars,
strip_accents=strip_accents,
**kwargs,
)
[docs] def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Creates a mask from the two sequences passed to be used in a sequence-pair classification task.
Funnel Transformer expects a sequence pair mask that has the following format:
::
2 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence | second sequence |
if token_ids_1 is None, only returns the first portion of the mask (0's).
Args:
token_ids_0 (:obj:`List[int]`):
List of ids.
token_ids_1 (:obj:`List[int]`, `optional`):
Optional second list of IDs for sequence pairs.
Returns:
:obj:`List[int]`: List of `token type IDs <../glossary.html#token-type-ids>`_ according to the given
sequence(s).
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls) * [self.cls_token_type_id] + len(token_ids_0 + sep) * [0]
return len(cls) * [self.cls_token_type_id] + len(token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
[docs]class FunnelTokenizerFast(BertTokenizerFast):
r"""
"Fast" tokenizer for the Funnel Transformer models (backed by HuggingFace's :obj:`tokenizers` library).
:class:`~transformers.FunnelTokenizerFast` is identical to :class:`~transformers.BertTokenizerFast` and runs
end-to-end tokenization: punctuation splitting + wordpiece.
Refer to superclass :class:`~transformers.BertTokenizerFast` for usage examples and documentation concerning
parameters.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
cls_token_type_id: int = 2
def __init__(
self,
vocab_file,
do_lower_case=True,
unk_token="<unk>",
sep_token="<sep>",
pad_token="<pad>",
cls_token="<cls>",
mask_token="<mask>",
bos_token="<s>",
eos_token="</s>",
clean_text=True,
tokenize_chinese_chars=True,
strip_accents=None,
wordpieces_prefix="##",
**kwargs
):
super().__init__(
vocab_file,
do_lower_case=do_lower_case,
unk_token=unk_token,
sep_token=sep_token,
pad_token=pad_token,
cls_token=cls_token,
mask_token=mask_token,
bos_token=bos_token,
eos_token=eos_token,
clean_text=clean_text,
tokenize_chinese_chars=tokenize_chinese_chars,
strip_accents=strip_accents,
wordpieces_prefix=wordpieces_prefix,
**kwargs,
)
[docs] def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Creates a mask from the two sequences passed to be used in a sequence-pair classification task.
Funnel Transformer expects a sequence pair mask that has the following format:
::
2 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence | second sequence |
if token_ids_1 is None, only returns the first portion of the mask (0's).
Args:
token_ids_0 (:obj:`List[int]`):
List of ids.
token_ids_1 (:obj:`List[int]`, `optional`):
Optional second list of IDs for sequence pairs.
Returns:
:obj:`List[int]`: List of `token type IDs <../glossary.html#token-type-ids>`_ according to the given
sequence(s).
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls) * [self.cls_token_type_id] + len(token_ids_0 + sep) * [0]
return len(cls) * [self.cls_token_type_id] + len(token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
def _convert_encoding(self, encoding, **kwargs):
# The fast tokenizer doesn't use the function above so we fix the cls token type id when decoding the fast
# tokenzier output.
encoding_dict = super()._convert_encoding(encoding, **kwargs)
if "token_type_ids" in encoding_dict:
# Note: we can't assume the <cls> token is in first position because left padding is a thing, hence the
# double list comprehension.
encoding_dict["token_type_ids"] = [
[self.cls_token_type_id if i == self.cls_token_id else t for i, t in zip(input_ids, type_ids)]
for input_ids, type_ids in zip(encoding_dict["input_ids"], encoding_dict["token_type_ids"])
]
return encoding_dict