# coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors 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,
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""" Tokenization classes for XLNet model."""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import logging
import os
from shutil import copyfile
import unicodedata
import six
from .tokenization_utils import PreTrainedTokenizer, clean_up_tokenization
logger = logging.getLogger(__name__)
VOCAB_FILES_NAMES = {'vocab_file': 'spiece.model'}
PRETRAINED_VOCAB_FILES_MAP = {
'vocab_file':
{
'xlnet-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-base-cased-spiece.model",
'xlnet-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-large-cased-spiece.model",
}
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
'xlnet-base-cased': None,
'xlnet-large-cased': None,
}
SPIECE_UNDERLINE = u'▁'
# Segments (not really needed)
SEG_ID_A = 0
SEG_ID_B = 1
SEG_ID_CLS = 2
SEG_ID_SEP = 3
SEG_ID_PAD = 4
[docs]class XLNetTokenizer(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, max_len=None,
do_lower_case=False, remove_space=True, keep_accents=False,
bos_token="<s>", eos_token="</s>", unk_token="<unk>", sep_token="<sep>",
pad_token="<pad>", cls_token="<cls>", mask_token="<mask>",
additional_special_tokens=["<eop>", "<eod>"], **kwargs):
super(XLNetTokenizer, self).__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, additional_special_tokens=
additional_special_tokens, **kwargs)
try:
import sentencepiece as spm
except ImportError:
logger.warning("You need to install SentencePiece to use XLNetTokenizer: https://github.com/google/sentencepiece"
"pip install sentencepiece")
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 XLNetTokenizer: https://github.com/google/sentencepiece"
"pip install sentencepiece")
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 six.PY2 and isinstance(outputs, str):
outputs = outputs.decode('utf-8')
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, return_unicode=True, sample=False):
""" Tokenize a string.
return_unicode is used only for py2
"""
text = self.preprocess_text(text)
# note(zhiliny): in some systems, sentencepiece only accepts str for py2
if six.PY2 and isinstance(text, unicode):
text = text.encode('utf-8')
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] == ',' 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)
# note(zhiliny): convert back to unicode for py2
if six.PY2 and return_unicode:
ret_pieces = []
for piece in new_pieces:
if isinstance(piece, str):
piece = piece.decode('utf-8')
ret_pieces.append(piece)
new_pieces = ret_pieces
return new_pieces
def _convert_token_to_id(self, token):
""" Converts a token (str/unicode) in an id using the vocab. """
return self.sp_model.PieceToId(token)
def _convert_id_to_token(self, index, return_unicode=True):
"""Converts an index (integer) in a token (string/unicode) using the vocab."""
token = self.sp_model.IdToPiece(index)
if six.PY2 and return_unicode and isinstance(token, str):
token = token.decode('utf-8')
return token
[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,)