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"""Fast Tokenization classes for Bert.""" |
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import json |
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from typing import List, Optional, Tuple |
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from tokenizers import normalizers |
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from ...tokenization_utils_fast import PreTrainedTokenizerFast |
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from ...utils import logging |
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from .tokenization_bert import BertTokenizer |
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logger = logging.get_logger(__name__) |
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VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"} |
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PRETRAINED_VOCAB_FILES_MAP = { |
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"vocab_file": { |
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"bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt", |
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"bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt", |
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"bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/vocab.txt", |
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"bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/vocab.txt", |
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"bert-base-multilingual-uncased": "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt", |
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"bert-base-multilingual-cased": "https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt", |
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"bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt", |
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"bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt", |
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"bert-large-uncased-whole-word-masking": "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt", |
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"bert-large-cased-whole-word-masking": "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt", |
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"bert-large-uncased-whole-word-masking-finetuned-squad": "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt", |
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"bert-large-cased-whole-word-masking-finetuned-squad": "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt", |
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"bert-base-cased-finetuned-mrpc": "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt", |
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"bert-base-german-dbmdz-cased": "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt", |
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"bert-base-german-dbmdz-uncased": "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt", |
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"TurkuNLP/bert-base-finnish-cased-v1": "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt", |
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"TurkuNLP/bert-base-finnish-uncased-v1": "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt", |
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"wietsedv/bert-base-dutch-cased": "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt", |
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}, |
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"tokenizer_file": { |
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"bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json", |
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"bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json", |
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"bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json", |
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"bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json", |
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"bert-base-multilingual-uncased": "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json", |
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"bert-base-multilingual-cased": "https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json", |
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"bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json", |
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"bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json", |
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"bert-large-uncased-whole-word-masking": "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json", |
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"bert-large-cased-whole-word-masking": "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json", |
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"bert-large-uncased-whole-word-masking-finetuned-squad": "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json", |
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"bert-large-cased-whole-word-masking-finetuned-squad": "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json", |
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"bert-base-cased-finetuned-mrpc": "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json", |
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"bert-base-german-dbmdz-cased": "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json", |
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"bert-base-german-dbmdz-uncased": "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json", |
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"TurkuNLP/bert-base-finnish-cased-v1": "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json", |
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"TurkuNLP/bert-base-finnish-uncased-v1": "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json", |
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"wietsedv/bert-base-dutch-cased": "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json", |
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}, |
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} |
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PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { |
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"bert-base-uncased": 512, |
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"bert-large-uncased": 512, |
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"bert-base-cased": 512, |
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"bert-large-cased": 512, |
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"bert-base-multilingual-uncased": 512, |
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"bert-base-multilingual-cased": 512, |
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"bert-base-chinese": 512, |
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"bert-base-german-cased": 512, |
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"bert-large-uncased-whole-word-masking": 512, |
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"bert-large-cased-whole-word-masking": 512, |
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"bert-large-uncased-whole-word-masking-finetuned-squad": 512, |
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"bert-large-cased-whole-word-masking-finetuned-squad": 512, |
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"bert-base-cased-finetuned-mrpc": 512, |
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"bert-base-german-dbmdz-cased": 512, |
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"bert-base-german-dbmdz-uncased": 512, |
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"TurkuNLP/bert-base-finnish-cased-v1": 512, |
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"TurkuNLP/bert-base-finnish-uncased-v1": 512, |
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"wietsedv/bert-base-dutch-cased": 512, |
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} |
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PRETRAINED_INIT_CONFIGURATION = { |
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"bert-base-uncased": {"do_lower_case": True}, |
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"bert-large-uncased": {"do_lower_case": True}, |
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"bert-base-cased": {"do_lower_case": False}, |
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"bert-large-cased": {"do_lower_case": False}, |
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"bert-base-multilingual-uncased": {"do_lower_case": True}, |
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"bert-base-multilingual-cased": {"do_lower_case": False}, |
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"bert-base-chinese": {"do_lower_case": False}, |
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"bert-base-german-cased": {"do_lower_case": False}, |
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"bert-large-uncased-whole-word-masking": {"do_lower_case": True}, |
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"bert-large-cased-whole-word-masking": {"do_lower_case": False}, |
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"bert-large-uncased-whole-word-masking-finetuned-squad": {"do_lower_case": True}, |
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"bert-large-cased-whole-word-masking-finetuned-squad": {"do_lower_case": False}, |
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"bert-base-cased-finetuned-mrpc": {"do_lower_case": False}, |
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"bert-base-german-dbmdz-cased": {"do_lower_case": False}, |
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"bert-base-german-dbmdz-uncased": {"do_lower_case": True}, |
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"TurkuNLP/bert-base-finnish-cased-v1": {"do_lower_case": False}, |
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"TurkuNLP/bert-base-finnish-uncased-v1": {"do_lower_case": True}, |
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"wietsedv/bert-base-dutch-cased": {"do_lower_case": False}, |
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} |
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class BertTokenizerFast(PreTrainedTokenizerFast): |
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r""" |
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Construct a "fast" BERT tokenizer (backed by HuggingFace's *tokenizers* library). Based on WordPiece. |
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This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should |
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refer to this superclass for more information regarding those methods. |
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Args: |
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vocab_file (`str`): |
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File containing the vocabulary. |
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do_lower_case (`bool`, *optional*, defaults to `True`): |
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Whether or not to lowercase the input when tokenizing. |
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unk_token (`str`, *optional*, defaults to `"[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 (`str`, *optional*, defaults to `"[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 (`str`, *optional*, defaults to `"[PAD]"`): |
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The token used for padding, for example when batching sequences of different lengths. |
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cls_token (`str`, *optional*, defaults to `"[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 (`str`, *optional*, defaults to `"[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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clean_text (`bool`, *optional*, defaults to `True`): |
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Whether or not to clean the text before tokenization by removing any control characters and replacing all |
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whitespaces by the classic one. |
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tokenize_chinese_chars (`bool`, *optional*, defaults to `True`): |
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Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (see [this |
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issue](https://github.com/huggingface/transformers/issues/328)). |
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strip_accents (`bool`, *optional*): |
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Whether or not to strip all accents. If this option is not specified, then it will be determined by the |
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value for `lowercase` (as in the original BERT). |
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wordpieces_prefix (`str`, *optional*, defaults to `"##"`): |
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The prefix for subwords. |
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""" |
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vocab_files_names = VOCAB_FILES_NAMES |
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pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP |
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pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION |
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max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES |
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slow_tokenizer_class = BertTokenizer |
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def __init__( |
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self, |
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vocab_file=None, |
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tokenizer_file=None, |
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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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tokenize_chinese_chars=True, |
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strip_accents=None, |
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**kwargs |
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): |
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super().__init__( |
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vocab_file, |
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tokenizer_file=tokenizer_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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tokenize_chinese_chars=tokenize_chinese_chars, |
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strip_accents=strip_accents, |
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**kwargs, |
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) |
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normalizer_state = json.loads(self.backend_tokenizer.normalizer.__getstate__()) |
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if ( |
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normalizer_state.get("lowercase", do_lower_case) != do_lower_case |
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or normalizer_state.get("strip_accents", strip_accents) != strip_accents |
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or normalizer_state.get("handle_chinese_chars", tokenize_chinese_chars) != tokenize_chinese_chars |
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): |
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normalizer_class = getattr(normalizers, normalizer_state.pop("type")) |
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normalizer_state["lowercase"] = do_lower_case |
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normalizer_state["strip_accents"] = strip_accents |
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normalizer_state["handle_chinese_chars"] = tokenize_chinese_chars |
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self.backend_tokenizer.normalizer = normalizer_class(**normalizer_state) |
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self.do_lower_case = do_lower_case |
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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. A BERT 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. |
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Returns: |
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`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. |
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""" |
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output = [self.cls_token_id] + token_ids_0 + [self.sep_token_id] |
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if token_ids_1: |
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output += token_ids_1 + [self.sep_token_id] |
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return output |
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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. A BERT sequence |
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pair mask has the following format: |
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``` |
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0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 |
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| first sequence | second sequence | |
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``` |
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If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s). |
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Args: |
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token_ids_0 (`List[int]`): |
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List of IDs. |
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token_ids_1 (`List[int]`, *optional*): |
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Optional second list of IDs for sequence pairs. |
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Returns: |
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`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given 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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def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: |
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files = self._tokenizer.model.save(save_directory, name=filename_prefix) |
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return tuple(files) |
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