Source code for transformers.models.funnel.tokenization_funnel_fast

# coding=utf-8
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#     http://www.apache.org/licenses/LICENSE-2.0
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""" Tokenization class for Funnel Transformer."""

from typing import List, Optional

from ...utils import logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_funnel import FunnelTokenizer


logger = logging.get_logger(__name__)

VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

_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://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt",
        "funnel-transformer/small-base": "https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt",
        "funnel-transformer/medium": "https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt",
        "funnel-transformer/medium-base": "https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt",
        "funnel-transformer/intermediate": "https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt",
        "funnel-transformer/intermediate-base": "https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt",
        "funnel-transformer/large": "https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt",
        "funnel-transformer/large-base": "https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt",
        "funnel-transformer/xlarge": "https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt",
        "funnel-transformer/xlarge-base": "https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt",
    },
    "tokenizer_file": {
        "funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json",
        "funnel-transformer/small-base": "https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json",
        "funnel-transformer/medium": "https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json",
        "funnel-transformer/medium-base": "https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json",
        "funnel-transformer/intermediate": "https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json",
        "funnel-transformer/intermediate-base": "https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json",
        "funnel-transformer/large": "https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json",
        "funnel-transformer/large-base": "https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json",
        "funnel-transformer/xlarge": "https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json",
        "funnel-transformer/xlarge-base": "https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json",
    },
}
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 FunnelTokenizerFast(BertTokenizerFast): r""" Construct a "fast" Funnel Transformer tokenizer (backed by HuggingFace's `tokenizers` library). :class:`~transformers.FunnelTokenizerFast` is identical to :class:`~transformers.BertTokenizerFast` and runs end-to-end tokenization: punctuation splitting and 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 slow_tokenizer_class = FunnelTokenizer cls_token_type_id: int = 2 def __init__( self, vocab_file=None, tokenizer_file=None, 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, tokenizer_file=tokenizer_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]: """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. A Funnel Transformer sequence pair mask 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 :obj:`token_ids_1` is :obj:`None`, this method only returns the first portion of the mask (0s). 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]