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bert_uncased_L-12_H-768_A-12_italian_alberto / tokenizer.py
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# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors, The HuggingFace Inc. team,
# and Marco Polignano.
#
# 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 Italian AlBERTo models."""
import collections
import logging
import os
import re
import logger
try:
from ekphrasis.classes.preprocessor import TextPreProcessor
from ekphrasis.classes.tokenizer import SocialTokenizer
from ekphrasis.dicts.emoticons import emoticons
except ImportError:
logger.warning(
"You need to install ekphrasis to use AlBERToTokenizer"
"pip install ekphrasis"
)
from pip._internal import main as pip
pip(['install', '--user', 'ekphrasis'])
from ekphrasis.classes.preprocessor import TextPreProcessor
from ekphrasis.classes.tokenizer import SocialTokenizer
from ekphrasis.dicts.emoticons import emoticons
try:
import numpy as np
except ImportError:
logger.warning(
"You need to install numpy to use AlBERToTokenizer"
"pip install numpy"
)
from pip._internal import main as pip
pip(['install', '--user', 'pandas'])
import pandas as pd
try:
from transformers import BertTokenizer, WordpieceTokenizer
from transformers.tokenization_bert import load_vocab
except ImportError:
logger.warning(
"You need to install pytorch-transformers to use AlBERToTokenizer"
"pip install pytorch-transformers"
)
from pip._internal import main as pip
pip(['install', '--user', 'pytorch-transformers'])
from transformers import BertTokenizer, WordpieceTokenizer
from transformers.tokenization_bert import load_vocab
text_processor = TextPreProcessor(
# terms that will be normalized
normalize=['url', 'email', 'user', 'percent', 'money', 'phone', 'time', 'date', 'number'],
# terms that will be annotated
annotate={"hashtag"},
fix_html=True, # fix HTML tokens
unpack_hashtags=True, # perform word segmentation on hashtags
# select a tokenizer. You can use SocialTokenizer, or pass your own
# the tokenizer, should take as input a string and return a list of tokens
tokenizer=SocialTokenizer(lowercase=True).tokenize,
dicts=[emoticons]
)
class AlBERTo_Preprocessing(object):
def __init__(self, do_lower_case=True, **kwargs):
self.do_lower_case = do_lower_case
def preprocess(self, text):
if self.do_lower_case:
text = text.lower()
text = str(" ".join(text_processor.pre_process_doc(text)))
text = re.sub(r'[^a-zA-ZÀ-ú</>!?♥♡\s\U00010000-\U0010ffff]', ' ', text)
text = re.sub(r'\s+', ' ', text)
text = re.sub(r'(\w)\1{2,}', r'\1\1', text)
text = re.sub(r'^\s', '', text)
text = re.sub(r'\s$', '', text)
return text
class AlBERToTokenizer(BertTokenizer):
def __init__(self, vocab_file, do_lower_case=True,
do_basic_tokenize=True, do_char_tokenize=False, do_wordpiece_tokenize=False, do_preprocessing = True, unk_token='[UNK]',
sep_token='[SEP]',
pad_token='[PAD]', cls_token='[CLS]', mask_token='[MASK]', **kwargs):
super(BertTokenizer, self).__init__(
unk_token=unk_token, sep_token=sep_token, pad_token=pad_token,
cls_token=cls_token, mask_token=mask_token, **kwargs)
self.do_wordpiece_tokenize = do_wordpiece_tokenize
self.do_lower_case = do_lower_case
self.vocab_file = vocab_file
self.do_basic_tokenize = do_basic_tokenize
self.do_char_tokenize = do_char_tokenize
self.unk_token = unk_token
self.do_preprocessing = do_preprocessing
if not os.path.isfile(vocab_file):
raise ValueError(
"Can't find a vocabulary file at path '{}'.".format(vocab_file))
self.vocab = load_vocab(vocab_file)
self.ids_to_tokens = collections.OrderedDict(
[(ids, tok) for tok, ids in self.vocab.items()])
if do_wordpiece_tokenize:
self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab,
unk_token=self.unk_token)
self.base_bert_tok = BertTokenizer(vocab_file=self.vocab_file, do_lower_case=do_lower_case,
unk_token=unk_token, sep_token=sep_token, pad_token=pad_token,
cls_token=cls_token, mask_token=mask_token, **kwargs)
def _convert_token_to_id(self, token):
"""Converts a token (str/unicode) to an id using the vocab."""
# if token[:2] == '##':
# token = token[2:]
return self.vocab.get(token, self.vocab.get(self.unk_token))
def convert_token_to_id(self, token):
return self._convert_token_to_id(token)
return self.vocab.get(token, self.vocab.get(self.unk_token))
def _convert_id_to_token(self, id):
# if token[:2] == '##':
# token = token[2:]
return list(self.vocab.keys())[int(id)]
def convert_id_to_token(self, id):
return self._convert_id_to_token(id)
def _convert_tokens_to_string(self,tokens):
"""Converts a sequence of tokens (string) to a single string."""
out_string = ' '.join(tokens).replace('##', '').strip()
return out_string
def convert_tokens_to_string(self,tokens):
return self._convert_tokens_to_string(tokens)
def _tokenize(self, text, never_split=None, **kwargs):
if self.do_preprocessing:
if self.do_lower_case:
text = text.lower()
text = str(" ".join(text_processor.pre_process_doc(text)))
text = re.sub(r'[^a-zA-ZÀ-ú</>!?♥♡\s\U00010000-\U0010ffff]', ' ', text)
text = re.sub(r'\s+', ' ', text)
text = re.sub(r'(\w)\1{2,}', r'\1\1', text)
text = re.sub(r'^\s', '', text)
text = re.sub(r'\s$', '', text)
# print(s)
split_tokens = [text]
if self.do_wordpiece_tokenize:
wordpiece_tokenizer = WordpieceTokenizer(self.vocab,self.unk_token)
split_tokens = wordpiece_tokenizer.tokenize(text)
elif self.do_char_tokenize:
tokenizer = CharacterTokenizer(self.vocab, self.unk_token)
split_tokens = tokenizer.tokenize(text)
elif self.do_basic_tokenize:
"""Tokenizes a piece of text."""
split_tokens = self.base_bert_tok.tokenize(text)
return split_tokens
def tokenize(self, text, never_split=None, **kwargs):
return self._tokenize(text, never_split)
class CharacterTokenizer(object):
"""Runs Character tokenziation."""
def __init__(self, vocab, unk_token,
max_input_chars_per_word=100, with_markers=True):
"""Constructs a CharacterTokenizer.
Args:
vocab: Vocabulary object.
unk_token: A special symbol for out-of-vocabulary token.
with_markers: If True, "#" is appended to each output character except the
first one.
"""
self.vocab = vocab
self.unk_token = unk_token
self.max_input_chars_per_word = max_input_chars_per_word
self.with_markers = with_markers
def tokenize(self, text):
"""Tokenizes a piece of text into characters.
For example:
input = "apple"
output = ["a", "##p", "##p", "##l", "##e"] (if self.with_markers is True)
output = ["a", "p", "p", "l", "e"] (if self.with_markers is False)
Args:
text: A single token or whitespace separated tokens.
This should have already been passed through `BasicTokenizer`.
Returns:
A list of characters.
"""
output_tokens = []
for i, char in enumerate(text):
if char not in self.vocab:
output_tokens.append(self.unk_token)
continue
if self.with_markers and i != 0:
output_tokens.append('##' + char)
else:
output_tokens.append(char)
return output_tokens
if __name__== "__main__":
a = AlBERTo_Preprocessing(do_lower_case=True)
s = "#IlGOverno presenta le linee guida sulla scuola #labuonascuola - http://t.co/SYS1T9QmQN"
b = a.preprocess(s)
print(b)
c =AlBERToTokenizer(do_lower_case=True,vocab_file="vocab.txt", do_preprocessing=True)
d = c.tokenize(s)
print(d)