Source code for transformers.tokenization_albert

# coding=utf-8
# Copyright 2018 Google AI, Google Brain and 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 classes for ALBERT model."""


import logging
import os
import unicodedata
from shutil import copyfile

from .tokenization_utils import PreTrainedTokenizer


logger = logging.getLogger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"}

PRETRAINED_VOCAB_FILES_MAP = {
    "vocab_file": {
        "albert-base-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-spiece.model",
        "albert-large-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-spiece.model",
        "albert-xlarge-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-spiece.model",
        "albert-xxlarge-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-spiece.model",
        "albert-base-v2": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-v2-spiece.model",
        "albert-large-v2": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-v2-spiece.model",
        "albert-xlarge-v2": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-v2-spiece.model",
        "albert-xxlarge-v2": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-v2-spiece.model",
    }
}

PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
    "albert-base-v1": 512,
    "albert-large-v1": 512,
    "albert-xlarge-v1": 512,
    "albert-xxlarge-v1": 512,
    "albert-base-v2": 512,
    "albert-large-v2": 512,
    "albert-xlarge-v2": 512,
    "albert-xxlarge-v2": 512,
}

SPIECE_UNDERLINE = "▁"


[docs]class AlbertTokenizer(PreTrainedTokenizer): """ 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, do_lower_case=True, remove_space=True, keep_accents=False, bos_token="[CLS]", eos_token="[SEP]", unk_token="<unk>", sep_token="[SEP]", pad_token="<pad>", cls_token="[CLS]", mask_token="[MASK]", **kwargs ): super().__init__( bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, sep_token=sep_token, pad_token=pad_token, cls_token=cls_token, mask_token=mask_token, **kwargs, ) self.max_len_single_sentence = self.max_len - 2 # take into account special tokens self.max_len_sentences_pair = self.max_len - 3 # take into account special tokens 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.do_lower_case = do_lower_case self.remove_space = remove_space self.keep_accents = keep_accents self.vocab_file = vocab_file self.sp_model = spm.SentencePieceProcessor() self.sp_model.Load(vocab_file) @property def vocab_size(self): return len(self.sp_model) 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) def preprocess_text(self, inputs): if self.remove_space: outputs = " ".join(inputs.strip().split()) else: outputs = inputs outputs = outputs.replace("``", '"').replace("''", '"') if not self.keep_accents: outputs = unicodedata.normalize("NFKD", outputs) outputs = "".join([c for c in outputs if not unicodedata.combining(c)]) if self.do_lower_case: outputs = outputs.lower() return outputs def _tokenize(self, text, sample=False): """ Tokenize a string. """ text = self.preprocess_text(text) if not sample: pieces = self.sp_model.EncodeAsPieces(text) else: pieces = self.sp_model.SampleEncodeAsPieces(text, 64, 0.1) new_pieces = [] for piece in pieces: if len(piece) > 1 and piece[-1] == str(",") and piece[-2].isdigit(): cur_pieces = self.sp_model.EncodeAsPieces(piece[:-1].replace(SPIECE_UNDERLINE, "")) if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE: if len(cur_pieces[0]) == 1: cur_pieces = cur_pieces[1:] else: cur_pieces[0] = cur_pieces[0][1:] cur_pieces.append(piece[-1]) new_pieces.extend(cur_pieces) else: new_pieces.append(piece) return new_pieces def _convert_token_to_id(self, token): """ Converts a token (str) in an id using the vocab. """ return self.sp_model.PieceToId(token) def _convert_id_to_token(self, index): """Converts an index (integer) in a token (str) using the vocab.""" return self.sp_model.IdToPiece(index)
[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 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. An ALBERT sequence has the following format: single sequence: [CLS] X [SEP] pair of sequences: [CLS] A [SEP] B [SEP] """ sep = [self.sep_token_id] cls = [self.cls_token_id] if token_ids_1 is None: return cls + token_ids_0 + sep return cls + token_ids_0 + 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]: 0 for a special token, 1 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 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, token_ids_1=None): """ Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT sequence pair mask has the following format: 0 0 0 0 0 0 0 0 0 0 0 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) * [0] + len(token_ids_1 + sep) * [1]
[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,)