Source code for transformers.models.splinter.tokenization_splinter

# coding=utf-8
# Copyright 2021 Tel AViv University, AllenAI and The HuggingFace Inc. team. All rights reserved.
# All rights reserved.
# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
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"""Tokenization classes for Splinter."""

import collections
import os
import unicodedata
from typing import List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging

logger = logging.get_logger(__name__)

VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}

    "vocab_file": {
        "tau/splinter-base": "",
        "tau/splinter-base-qass": "",
        "tau/splinter-large": "",
        "tau/splinter-large-qass": "",

    "tau/splinter-base": 512,
    "tau/splinter-base-qass": 512,
    "tau/splinter-large": 512,
    "tau/splinter-large-qass": 512,

    "tau/splinter-base": {"do_lower_case": False},
    "tau/splinter-base-qass": {"do_lower_case": False},
    "tau/splinter-large": {"do_lower_case": False},
    "tau/splinter-large-qass": {"do_lower_case": False},

def load_vocab(vocab_file):
    """Loads a vocabulary file into a dictionary."""
    vocab = collections.OrderedDict()
    with open(vocab_file, "r", encoding="utf-8") as reader:
        tokens = reader.readlines()
    for index, token in enumerate(tokens):
        token = token.rstrip("\n")
        vocab[token] = index
    return vocab

def whitespace_tokenize(text):
    """Runs basic whitespace cleaning and splitting on a piece of text."""
    text = text.strip()
    if not text:
        return []
    tokens = text.split()
    return tokens

[docs]class SplinterTokenizer(PreTrainedTokenizer): r""" Construct a Splinter tokenizer. Based on WordPiece. This tokenizer inherits from :class:`~transformers.PreTrainedTokenizer` which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: vocab_file (:obj:`str`): File containing the vocabulary. do_lower_case (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether or not to lowercase the input when tokenizing. do_basic_tokenize (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether or not to do basic tokenization before WordPiece. never_split (:obj:`Iterable`, `optional`): Collection of tokens which will never be split during tokenization. Only has an effect when :obj:`do_basic_tokenize=True` unk_token (:obj:`str`, `optional`, defaults to :obj:`"[UNK]"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. sep_token (:obj:`str`, `optional`, defaults to :obj:`"[SEP]"`): The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens. pad_token (:obj:`str`, `optional`, defaults to :obj:`"[PAD]"`): The token used for padding, for example when batching sequences of different lengths. cls_token (:obj:`str`, `optional`, defaults to :obj:`"[CLS]"`): The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens. mask_token (:obj:`str`, `optional`, defaults to :obj:`"[MASK]"`): The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict. question_token (:obj:`str`, `optional`, defaults to :obj:`"[QUESTION]"`): The token used for constructing question representations. tokenize_chinese_chars (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (see this `issue <>`__). strip_accents: (:obj:`bool`, `optional`): Whether or not to strip all accents. If this option is not specified, then it will be determined by the value for :obj:`lowercase` (as in the original BERT). """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES 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]", question_token="[QUESTION]", tokenize_chinese_chars=True, strip_accents=None, **kwargs ): super().__init__( 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, tokenize_chinese_chars=tokenize_chinese_chars, strip_accents=strip_accents, **kwargs, ) if not os.path.isfile(vocab_file): raise ValueError( f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained " "model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`" ) self.vocab = load_vocab(vocab_file) self.ids_to_tokens = collections.OrderedDict([(ids, tok) for tok, ids in self.vocab.items()]) self.do_basic_tokenize = do_basic_tokenize if do_basic_tokenize: self.basic_tokenizer = BasicTokenizer( do_lower_case=do_lower_case, never_split=never_split, tokenize_chinese_chars=tokenize_chinese_chars, strip_accents=strip_accents, ) self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=self.unk_token) self.question_token = question_token @property def question_token_id(self): """ :obj:`Optional[int]`: Id of the question token in the vocabulary, used to condition the answer on a question representation. """ return self.convert_tokens_to_ids(self.question_token) @property def do_lower_case(self): return self.basic_tokenizer.do_lower_case @property def vocab_size(self): return len(self.vocab) def get_vocab(self): return dict(self.vocab, **self.added_tokens_encoder) def _tokenize(self, text): split_tokens = [] if self.do_basic_tokenize: for token in self.basic_tokenizer.tokenize(text, never_split=self.all_special_tokens): # If the token is part of the never_split set if token in self.basic_tokenizer.never_split: split_tokens.append(token) else: split_tokens += self.wordpiece_tokenizer.tokenize(token) else: split_tokens = self.wordpiece_tokenizer.tokenize(text) return split_tokens def _convert_token_to_id(self, token): """Converts a token (str) in an id using the vocab.""" return self.vocab.get(token, self.vocab.get(self.unk_token)) def _convert_id_to_token(self, index): """Converts an index (integer) in a token (str) using the vocab.""" return self.ids_to_tokens.get(index, self.unk_token) def convert_tokens_to_string(self, tokens): """Converts a sequence of tokens (string) in a single string.""" out_string = " ".join(tokens).replace(" ##", "").strip() return out_string
[docs] def build_inputs_with_special_tokens( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Build model inputs from a pair of sequence for question answering tasks by concatenating and adding special tokens. A Splinter sequence has the following format: - single sequence: ``[CLS] X [SEP]`` - pair of sequences for question answering: ``[CLS] question_tokens [QUESTION] . [SEP] context_tokens [SEP]`` Args: token_ids_0 (:obj:`List[int]`): The question token IDs if pad_on_right, else context tokens IDs token_ids_1 (:obj:`List[int]`, `optional`): The context token IDs if pad_on_right, else question token IDs Returns: :obj:`List[int]`: List of `input IDs <../glossary.html#input-ids>`__ with the appropriate special tokens. """ if token_ids_1 is None: return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] cls = [self.cls_token_id] sep = [self.sep_token_id] question_suffix = [self.question_token_id] + [self.convert_tokens_to_ids(".")] if self.padding_side == "right": # Input is question-then-context return cls + token_ids_0 + question_suffix + sep + token_ids_1 + sep else: # Input is context-then-question return cls + token_ids_0 + sep + token_ids_1 + question_suffix + sep
[docs] def get_special_tokens_mask( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False ) -> List[int]: """ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer ``prepare_for_model`` method. 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. already_has_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not the token list is already formatted with special tokens for the model. Returns: :obj:`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. """ if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True ) if token_ids_1 is not None: return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1] return [1] + ([0] * len(token_ids_0)) + [1]
[docs] def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Create the token type IDs corresponding to the sequences passed. `What are token type IDs? <../glossary.html#token-type-ids>`__ Should be overridden in a subclass if the model has a special way of building those. Args: token_ids_0 (:obj:`List[int]`): The first tokenized sequence. token_ids_1 (:obj:`List[int]`, `optional`): The second tokenized sequence. Returns: :obj:`List[int]`: The token type ids. """ sep = [self.sep_token_id] cls = [self.cls_token_id] question_suffix = [self.question_token_id] + [self.convert_tokens_to_ids(".")] if token_ids_1 is None: return len(cls + token_ids_0 + sep) * [0] if self.padding_side == "right": # Input is question-then-context return len(cls + token_ids_0 + question_suffix + sep) * [0] + len(token_ids_1 + sep) * [1] else: # Input is context-then-question return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + question_suffix + sep) * [1]
[docs] def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: index = 0 if os.path.isdir(save_directory): vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) else: vocab_file = (filename_prefix + "-" if filename_prefix else "") + save_directory with open(vocab_file, "w", encoding="utf-8") as writer: for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]): if index != token_index: logger.warning( f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive." " Please check that the vocabulary is not corrupted!" ) index = token_index writer.write(token + "\n") index += 1 return (vocab_file,)
class BasicTokenizer(object): """ Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.). Args: do_lower_case (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether or not to lowercase the input when tokenizing. never_split (:obj:`Iterable`, `optional`): Collection of tokens which will never be split during tokenization. Only has an effect when :obj:`do_basic_tokenize=True` tokenize_chinese_chars (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (see this `issue <>`__). strip_accents: (:obj:`bool`, `optional`): Whether or not to strip all accents. If this option is not specified, then it will be determined by the value for :obj:`lowercase` (as in the original BERT). """ def __init__(self, do_lower_case=True, never_split=None, tokenize_chinese_chars=True, strip_accents=None): if never_split is None: never_split = [] self.do_lower_case = do_lower_case self.never_split = set(never_split) self.tokenize_chinese_chars = tokenize_chinese_chars self.strip_accents = strip_accents def tokenize(self, text, never_split=None): """ Basic Tokenization of a piece of text. Split on "white spaces" only, for sub-word tokenization, see WordPieceTokenizer. Args: **never_split**: (`optional`) list of str Kept for backward compatibility purposes. Now implemented directly at the base class level (see :func:`PreTrainedTokenizer.tokenize`) List of token not to split. """ # union() returns a new set by concatenating the two sets. never_split = self.never_split.union(set(never_split)) if never_split else self.never_split text = self._clean_text(text) # This was added on November 1st, 2018 for the multilingual and Chinese # models. This is also applied to the English models now, but it doesn't # matter since the English models were not trained on any Chinese data # and generally don't have any Chinese data in them (there are Chinese # characters in the vocabulary because Wikipedia does have some Chinese # words in the English Wikipedia.). if self.tokenize_chinese_chars: text = self._tokenize_chinese_chars(text) orig_tokens = whitespace_tokenize(text) split_tokens = [] for token in orig_tokens: if token not in never_split: if self.do_lower_case: token = token.lower() if self.strip_accents is not False: token = self._run_strip_accents(token) elif self.strip_accents: token = self._run_strip_accents(token) split_tokens.extend(self._run_split_on_punc(token, never_split)) output_tokens = whitespace_tokenize(" ".join(split_tokens)) return output_tokens def _run_strip_accents(self, text): """Strips accents from a piece of text.""" text = unicodedata.normalize("NFD", text) output = [] for char in text: cat = unicodedata.category(char) if cat == "Mn": continue output.append(char) return "".join(output) def _run_split_on_punc(self, text, never_split=None): """Splits punctuation on a piece of text.""" if never_split is not None and text in never_split: return [text] chars = list(text) i = 0 start_new_word = True output = [] while i < len(chars): char = chars[i] if _is_punctuation(char): output.append([char]) start_new_word = True else: if start_new_word: output.append([]) start_new_word = False output[-1].append(char) i += 1 return ["".join(x) for x in output] def _tokenize_chinese_chars(self, text): """Adds whitespace around any CJK character.""" output = [] for char in text: cp = ord(char) if self._is_chinese_char(cp): output.append(" ") output.append(char) output.append(" ") else: output.append(char) return "".join(output) def _is_chinese_char(self, cp): """Checks whether CP is the codepoint of a CJK character.""" # This defines a "chinese character" as anything in the CJK Unicode block: # # # Note that the CJK Unicode block is NOT all Japanese and Korean characters, # despite its name. The modern Korean Hangul alphabet is a different block, # as is Japanese Hiragana and Katakana. Those alphabets are used to write # space-separated words, so they are not treated specially and handled # like the all of the other languages. if ( (cp >= 0x4E00 and cp <= 0x9FFF) or (cp >= 0x3400 and cp <= 0x4DBF) # or (cp >= 0x20000 and cp <= 0x2A6DF) # or (cp >= 0x2A700 and cp <= 0x2B73F) # or (cp >= 0x2B740 and cp <= 0x2B81F) # or (cp >= 0x2B820 and cp <= 0x2CEAF) # or (cp >= 0xF900 and cp <= 0xFAFF) or (cp >= 0x2F800 and cp <= 0x2FA1F) # ): # return True return False def _clean_text(self, text): """Performs invalid character removal and whitespace cleanup on text.""" output = [] for char in text: cp = ord(char) if cp == 0 or cp == 0xFFFD or _is_control(char): continue if _is_whitespace(char): output.append(" ") else: output.append(char) return "".join(output) class WordpieceTokenizer(object): """Runs WordPiece tokenization.""" def __init__(self, vocab, unk_token, max_input_chars_per_word=100): self.vocab = vocab self.unk_token = unk_token self.max_input_chars_per_word = max_input_chars_per_word def tokenize(self, text): """ Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform tokenization using the given vocabulary. For example, :obj:`input = "unaffable"` wil return as output :obj:`["un", "##aff", "##able"]`. Args: text: A single token or whitespace separated tokens. This should have already been passed through `BasicTokenizer`. Returns: A list of wordpiece tokens. """ output_tokens = [] for token in whitespace_tokenize(text): chars = list(token) if len(chars) > self.max_input_chars_per_word: output_tokens.append(self.unk_token) continue is_bad = False start = 0 sub_tokens = [] while start < len(chars): end = len(chars) cur_substr = None while start < end: substr = "".join(chars[start:end]) if start > 0: substr = "##" + substr if substr in self.vocab: cur_substr = substr break end -= 1 if cur_substr is None: is_bad = True break sub_tokens.append(cur_substr) start = end if is_bad: output_tokens.append(self.unk_token) else: output_tokens.extend(sub_tokens) return output_tokens