Source code for transformers.tokenization_camembert

# coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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""" Tokenization classes for Camembert model."""


import logging
import os
from shutil import copyfile

import sentencepiece as spm

from transformers.tokenization_utils import PreTrainedTokenizer

from .tokenization_xlnet import SPIECE_UNDERLINE


logger = logging.getLogger(__name__)

VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model"}

PRETRAINED_VOCAB_FILES_MAP = {
    "vocab_file": {
        "camembert-base": "https://s3.amazonaws.com/models.huggingface.co/bert/camembert-base-sentencepiece.bpe.model",
    }
}

PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
    "camembert-base": None,
}

SHARED_MODEL_IDENTIFIERS = [
    # Load with
    # `tokenizer = AutoTokenizer.from_pretrained("username/pretrained_model")`
    "Musixmatch/umberto-commoncrawl-cased-v1",
    "Musixmatch/umberto-wikipedia-uncased-v1",
]


[docs]class CamembertTokenizer(PreTrainedTokenizer): """ Adapted from RobertaTokenizer and XLNetTokenizer SentencePiece based tokenizer. Peculiarities: - requires `SentencePiece <https://github.com/google/sentencepiece>`_ """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES def __init__( self, vocab_file, bos_token="<s>", eos_token="</s>", sep_token="</s>", cls_token="<s>", unk_token="<unk>", pad_token="<pad>", mask_token="<mask>", additional_special_tokens=["<s>NOTUSED", "</s>NOTUSED"], **kwargs ): super().__init__( max_len=512, bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, sep_token=sep_token, cls_token=cls_token, pad_token=pad_token, mask_token=mask_token, additional_special_tokens=additional_special_tokens, **kwargs, ) self.max_len_single_sentence = self.max_len - 2 # take into account special tokens self.max_len_sentences_pair = self.max_len - 4 # take into account special tokens self.sp_model = spm.SentencePieceProcessor() self.sp_model.Load(str(vocab_file)) self.vocab_file = vocab_file # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual # sentencepiece vocabulary (this is the case for <s> and </s> self.fairseq_tokens_to_ids = {"<s>NOTUSED": 0, "<pad>": 1, "</s>NOTUSED": 2, "<unk>": 3} self.fairseq_offset = len(self.fairseq_tokens_to_ids) self.fairseq_tokens_to_ids["<mask>"] = len(self.sp_model) + len(self.fairseq_tokens_to_ids) self.fairseq_ids_to_tokens = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
[docs] def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A RoBERTa sequence has the following format: single sequence: <s> X </s> pair of sequences: <s> A </s></s> B </s> """ 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] return cls + token_ids_0 + sep + sep + token_ids_1 + sep
[docs] def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False): """ Retrieves 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`` or ``encode_plus`` methods. Args: token_ids_0: list of ids (must not contain special tokens) token_ids_1: Optional list of ids (must not contain special tokens), necessary when fetching sequence ids for sequence pairs already_has_special_tokens: (default False) Set to True if the token list is already formated with special tokens for the model Returns: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. """ if already_has_special_tokens: if token_ids_1 is not None: raise ValueError( "You should not supply a second sequence if the provided sequence of " "ids is already formated with special tokens for the model." ) return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0)) if token_ids_1 is None: return [1] + ([0] * len(token_ids_0)) + [1] return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
[docs] def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None): """ Creates a mask from the two sequences passed to be used in a sequence-pair classification task. A RoBERTa sequence pair mask has the following format: 0 0 0 0 0 0 0 0 0 0 1 1 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). """ sep = [self.sep_token_id] cls = [self.cls_token_id] if token_ids_1 is None: return len(cls + token_ids_0 + sep) * [0] return len(cls + token_ids_0 + sep + sep) * [0] + len(token_ids_1 + sep) * [1]
@property def vocab_size(self): return len(self.fairseq_tokens_to_ids) + len(self.sp_model) def _tokenize(self, text): return self.sp_model.EncodeAsPieces(text) def _convert_token_to_id(self, token): """ Converts a token (str) in an id using the vocab. """ if token in self.fairseq_tokens_to_ids: return self.fairseq_tokens_to_ids[token] elif self.sp_model.PieceToId(token) == 0: # Convert sentence piece unk token to fairseq unk token index return self.unk_token_id return self.fairseq_offset + self.sp_model.PieceToId(token) def _convert_id_to_token(self, index): """Converts an index (integer) in a token (str) using the vocab.""" if index in self.fairseq_ids_to_tokens: return self.fairseq_ids_to_tokens[index] return self.sp_model.IdToPiece(index - self.fairseq_offset) def __getstate__(self): state = self.__dict__.copy() state["sp_model"] = None return state def __setstate__(self, d): self.__dict__ = d try: import sentencepiece as spm except ImportError: logger.warning( "You need to install SentencePiece to use AlbertTokenizer: https://github.com/google/sentencepiece" "pip install sentencepiece" ) raise self.sp_model = spm.SentencePieceProcessor() self.sp_model.Load(self.vocab_file)
[docs] def convert_tokens_to_string(self, tokens): """Converts a sequence of tokens (strings for sub-words) in a single string.""" out_string = "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip() return out_string
[docs] def save_vocabulary(self, save_directory): """ Save the sentencepiece vocabulary (copy original file) and special tokens file to a directory. """ if not os.path.isdir(save_directory): logger.error("Vocabulary path ({}) should be a directory".format(save_directory)) return out_vocab_file = os.path.join(save_directory, VOCAB_FILES_NAMES["vocab_file"]) if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file): copyfile(self.vocab_file, out_vocab_file) return (out_vocab_file,)