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# Copyright (c) 2020, VinAI Research and the HuggingFace Inc. team.
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# Copyright 2018 The Open AI Team Authors and The HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Tokenization classes for BERTweet"""
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import html
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import os
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import re
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import regex
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from ...tokenization_python import PreTrainedTokenizer
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from ...utils import logging
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logger = logging.get_logger(__name__)
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VOCAB_FILES_NAMES = {
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"vocab_file": "vocab.txt",
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"merges_file": "bpe.codes",
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}
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def get_pairs(word):
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"""
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Return set of symbol pairs in a word.
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Word is represented as tuple of symbols (symbols being variable-length strings).
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"""
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pairs = set()
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prev_char = word[0]
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for char in word[1:]:
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pairs.add((prev_char, char))
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prev_char = char
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pairs = set(pairs)
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return pairs
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class BertweetTokenizer(PreTrainedTokenizer):
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"""
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Constructs a BERTweet tokenizer, using Byte-Pair-Encoding.
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This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
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this superclass for more information regarding those methods.
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Args:
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vocab_file (`str`):
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Path to the vocabulary file.
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merges_file (`str`):
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Path to the merges file.
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normalization (`bool`, *optional*, defaults to `False`):
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Whether or not to apply a normalization preprocess.
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bos_token (`str`, *optional*, defaults to `"<s>"`):
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The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
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<Tip>
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When building a sequence using special tokens, this is not the token that is used for the beginning of
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sequence. The token used is the `cls_token`.
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</Tip>
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eos_token (`str`, *optional*, defaults to `"</s>"`):
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The end of sequence token.
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<Tip>
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When building a sequence using special tokens, this is not the token that is used for the end of sequence.
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The token used is the `sep_token`.
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</Tip>
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sep_token (`str`, *optional*, defaults to `"</s>"`):
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The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
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sequence classification or for a text and a question for question answering. It is also used as the last
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token of a sequence built with special tokens.
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cls_token (`str`, *optional*, defaults to `"<s>"`):
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The classifier token which is used when doing sequence classification (classification of the whole sequence
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instead of per-token classification). It is the first token of the sequence when built with special tokens.
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unk_token (`str`, *optional*, defaults to `"<unk>"`):
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The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
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token instead.
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pad_token (`str`, *optional*, defaults to `"<pad>"`):
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The token used for padding, for example when batching sequences of different lengths.
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mask_token (`str`, *optional*, defaults to `"<mask>"`):
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The token used for masking values. This is the token used when training this model with masked language
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modeling. This is the token which the model will try to predict.
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"""
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vocab_files_names = VOCAB_FILES_NAMES
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def __init__(
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self,
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vocab_file,
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merges_file,
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normalization=False,
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bos_token="<s>",
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eos_token="</s>",
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sep_token="</s>",
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cls_token="<s>",
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unk_token="<unk>",
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pad_token="<pad>",
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mask_token="<mask>",
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**kwargs,
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):
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try:
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from emoji import demojize
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self.demojizer = demojize
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except ImportError:
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logger.warning(
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"emoji is not installed, thus not converting emoticons or emojis into text. Install emoji: pip3"
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" install emoji==0.6.0"
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)
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self.demojizer = None
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self.vocab_file = vocab_file
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self.merges_file = merges_file
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self.encoder = {}
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self.encoder[str(bos_token)] = 0
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self.encoder[str(pad_token)] = 1
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self.encoder[str(eos_token)] = 2
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self.encoder[str(unk_token)] = 3
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self.add_from_file(vocab_file)
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self.decoder = {v: k for k, v in self.encoder.items()}
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with open(merges_file, encoding="utf-8") as merges_handle:
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merges = merges_handle.read().split("\n")[:-1]
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merges = [tuple(merge.split()[:-1]) for merge in merges]
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self.bpe_ranks = dict(zip(merges, range(len(merges))))
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self.cache = {}
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self.normalization = normalization
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self.tweetPreprocessor = TweetTokenizer()
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self.special_puncts = {"’": "'", "…": "..."}
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super().__init__(
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normalization=normalization,
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bos_token=bos_token,
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eos_token=eos_token,
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sep_token=sep_token,
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cls_token=cls_token,
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unk_token=unk_token,
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pad_token=pad_token,
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mask_token=mask_token,
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# Configure patterns instead of overriding methods
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token_type_ids_pattern="all_zeros", # BERTweet doesn't use token type IDs
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token_type_ids_include_special_tokens=True,
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special_tokens_pattern="cls_double_sep", # <s> X </s></s> Y </s>
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**kwargs,
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)
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@property
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def vocab_size(self):
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return len(self.encoder)
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def get_vocab(self):
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return dict(self.encoder, **self.added_tokens_encoder)
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def bpe(self, token):
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if token in self.cache:
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return self.cache[token]
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word = tuple(token)
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word = tuple(list(word[:-1]) + [word[-1] + "</w>"])
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pairs = get_pairs(word)
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if not pairs:
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return token
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while True:
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bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
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if bigram not in self.bpe_ranks:
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break
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first, second = bigram
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new_word = []
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i = 0
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while i < len(word):
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try:
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j = word.index(first, i)
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except ValueError:
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new_word.extend(word[i:])
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break
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else:
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new_word.extend(word[i:j])
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i = j
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if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
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new_word.append(first + second)
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i += 2
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else:
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new_word.append(word[i])
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i += 1
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new_word = tuple(new_word)
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word = new_word
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if len(word) == 1:
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break
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else:
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pairs = get_pairs(word)
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word = "@@ ".join(word)
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word = word[:-4]
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self.cache[token] = word
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return word
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def _tokenize(self, text):
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"""Tokenize a string."""
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if self.normalization: # Perform Tweet normalization before performing BPE
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text = self.normalizeTweet(text)
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split_tokens = []
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words = re.findall(r"\S+\n?", text)
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for token in words:
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split_tokens.extend(list(self.bpe(token).split(" ")))
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return split_tokens
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def normalizeTweet(self, tweet):
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"""
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Normalize a raw Tweet
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"""
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for punct in self.special_puncts:
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tweet = tweet.replace(punct, self.special_puncts[punct])
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tokens = self.tweetPreprocessor.tokenize(tweet)
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normTweet = " ".join([self.normalizeToken(token) for token in tokens])
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normTweet = (
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normTweet.replace("cannot ", "can not ")
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.replace("n't ", " n't ")
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.replace("n 't ", " n't ")
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.replace("ca n't", "can't")
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.replace("ai n't", "ain't")
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)
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normTweet = (
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normTweet.replace("'m ", " 'm ")
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.replace("'re ", " 're ")
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.replace("'s ", " 's ")
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.replace("'ll ", " 'll ")
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.replace("'d ", " 'd ")
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.replace("'ve ", " 've ")
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)
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normTweet = (
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normTweet.replace(" p . m .", " p.m.")
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.replace(" p . m ", " p.m ")
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.replace(" a . m .", " a.m.")
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.replace(" a . m ", " a.m ")
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)
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return " ".join(normTweet.split())
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def normalizeToken(self, token):
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"""
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Normalize tokens in a Tweet
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"""
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lowercased_token = token.lower()
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if token.startswith("@"):
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return "@USER"
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elif lowercased_token.startswith("http") or lowercased_token.startswith("www"):
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return "HTTPURL"
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elif len(token) == 1:
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if token in self.special_puncts:
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return self.special_puncts[token]
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if self.demojizer is not None:
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return self.demojizer(token)
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else:
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return token
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else:
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return token
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def _convert_token_to_id(self, token):
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"""Converts a token (str) in an id using the vocab."""
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return self.encoder.get(token, self.encoder.get(self.unk_token))
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def _convert_id_to_token(self, index):
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"""Converts an index (integer) in a token (str) using the vocab."""
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return self.decoder.get(index, self.unk_token)
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def convert_tokens_to_string(self, tokens):
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"""Converts a sequence of tokens (string) in a single string."""
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out_string = " ".join(tokens).replace("@@ ", "").strip()
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return out_string
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# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
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# filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
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# tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
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# tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
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# return ''.join(tokens_generated_so_far)
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def save_vocabulary(self, save_directory: str, filename_prefix: str | None = None) -> tuple[str, ...]:
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"""
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Save the vocabulary and merges files to a directory.
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"""
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if not os.path.isdir(save_directory):
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logger.error(f"Vocabulary path ({save_directory}) should be a directory")
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return ()
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vocab_files_names = getattr(self, "vocab_files_names", {})
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prefix = f"{filename_prefix}-" if filename_prefix else ""
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# Save vocabulary in the format expected by add_from_file: <token> <id>
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# Exclude special tokens (IDs 0-3) as they are added in __init__ before add_from_file
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vocab_file = os.path.join(save_directory, prefix + vocab_files_names.get("vocab_file", "vocab.txt"))
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with open(vocab_file, "w", encoding="utf-8") as f:
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for token, token_id in sorted(self.encoder.items(), key=lambda kv: kv[1]):
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# Only save tokens with ID >= 4, as IDs 0-3 are reserved for special tokens
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if token_id >= 4:
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f.write(f"{token} {token_id}\n")
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# Save BPE merges
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merge_file = os.path.join(save_directory, prefix + vocab_files_names.get("merges_file", "bpe.codes"))
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with open(merge_file, "w", encoding="utf-8") as writer:
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writer.writelines(
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" ".join(bpe_tokens) + "\n"
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for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1])
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)
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return (vocab_file, merge_file)
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def add_from_file(self, f):
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"""
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Loads a pre-existing dictionary from a text file and adds its symbols to this instance.
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"""
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if isinstance(f, str):
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try:
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with open(f, "r", encoding="utf-8") as fd:
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self.add_from_file(fd)
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except FileNotFoundError as fnfe:
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raise fnfe
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except UnicodeError:
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raise Exception(f"Incorrect encoding detected in {f}, please rebuild the dataset")
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return
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lines = f.readlines()
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for lineTmp in lines:
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line = lineTmp.strip()
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idx = line.rfind(" ")
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if idx == -1:
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raise ValueError("Incorrect dictionary format, expected '<token> <cnt>'")
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word = line[:idx]
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self.encoder[word] = len(self.encoder)
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# Natural Language Toolkit: Twitter Tokenizer
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#
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# Copyright (C) 2001-2020 NLTK Project
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# Author: Christopher Potts <cgpotts@stanford.edu>
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# Ewan Klein <ewan@inf.ed.ac.uk> (modifications)
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# Pierpaolo Pantone <> (modifications)
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# URL: http://nltk.org/
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# For license information, see LICENSE.TXT
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#
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"""
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Twitter-aware tokenizer, designed to be flexible and easy to adapt to new domains and tasks. The basic logic is this:
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1. The tuple regex_strings defines a list of regular expression strings.
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2. The regex_strings strings are put, in order, into a compiled regular expression object called word_re.
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3. The tokenization is done by word_re.findall(s), where s is the user-supplied string, inside the tokenize() method of
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the class Tokenizer.
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4. When instantiating Tokenizer objects, there is a single option: preserve_case. By default, it is set to True. If it
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is set to False, then the tokenizer will lowercase everything except for emoticons.
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"""
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######################################################################
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#
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# import regex # https://github.com/nltk/nltk/issues/2409
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# import html
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#
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######################################################################
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# The following strings are components in the regular expression
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# that is used for tokenizing. It's important that phone_number
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# appears first in the final regex (since it can contain whitespace).
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# It also could matter that tags comes after emoticons, due to the
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# possibility of having text like
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#
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# <:| and some text >:)
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#
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# Most importantly, the final element should always be last, since it
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# does a last ditch whitespace-based tokenization of whatever is left.
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# ToDo: Update with http://en.wikipedia.org/wiki/List_of_emoticons ?
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# This particular element is used in a couple ways, so we define it
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# with a name:
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# docstyle-ignore
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EMOTICONS = r"""
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(?:
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[<>]?
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[:;=8] # eyes
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[\-o\*\']? # optional nose
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[\)\]\(\[dDpP/\:\}\{@\|\\] # mouth
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[\)\]\(\[dDpP/\:\}\{@\|\\] # mouth
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[\-o\*\']? # optional nose
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[:;=8] # eyes
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[<>]?
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<3 # heart
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)"""
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# URL pattern due to John Gruber, modified by Tom Winzig. See
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# https://gist.github.com/winzig/8894715
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# docstyle-ignore
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URLS = r""" # Capture 1: entire matched URL
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(?:
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https?: # URL protocol and colon
|
|
|
(?:
|
|
|
/{1,3} # 1-3 slashes
|
|
|
| # or
|
|
|
[a-z0-9%] # Single letter or digit or '%'
|
|
|
# (Trying not to match e.g. "URI::Escape")
|
|
|
)
|
|
|
| # or
|
|
|
# looks like domain name followed by a slash:
|
|
|
[a-z0-9.\-]+[.]
|
|
|
(?:[a-z]{2,13})
|
|
|
/
|
|
|
)
|
|
|
(?: # One or more:
|
|
|
[^\s()<>{}\[\]]+ # Run of non-space, non-()<>{}[]
|
|
|
| # or
|
|
|
\([^\s()]*?\([^\s()]+\)[^\s()]*?\) # balanced parens, one level deep: (...(...)...)
|
|
|
|
|
|
|
\([^\s]+?\) # balanced parens, non-recursive: (...)
|
|
|
)+
|
|
|
(?: # End with:
|
|
|
\([^\s()]*?\([^\s()]+\)[^\s()]*?\) # balanced parens, one level deep: (...(...)...)
|
|
|
|
|
|
|
\([^\s]+?\) # balanced parens, non-recursive: (...)
|
|
|
| # or
|
|
|
[^\s`!()\[\]{};:'".,<>?«»“”‘’] # not a space or one of these punct chars
|
|
|
)
|
|
|
| # OR, the following to match naked domains:
|
|
|
(?:
|
|
|
(?<!@) # not preceded by a @, avoid matching foo@_gmail.com_
|
|
|
[a-z0-9]+
|
|
|
(?:[.\-][a-z0-9]+)*
|
|
|
[.]
|
|
|
(?:[a-z]{2,13})
|
|
|
\b
|
|
|
/?
|
|
|
(?!@) # not succeeded by a @,
|
|
|
# avoid matching "foo.na" in "foo.na@example.com"
|
|
|
)
|
|
|
"""
|
|
|
|
|
|
# docstyle-ignore
|
|
|
# The components of the tokenizer:
|
|
|
REGEXPS = (
|
|
|
URLS,
|
|
|
# Phone numbers:
|
|
|
r"""
|
|
|
(?:
|
|
|
(?: # (international)
|
|
|
\+?[01]
|
|
|
[ *\-.\)]*
|
|
|
)?
|
|
|
(?: # (area code)
|
|
|
[\(]?
|
|
|
\d{3}
|
|
|
[ *\-.\)]*
|
|
|
)?
|
|
|
\d{3} # exchange
|
|
|
[ *\-.\)]*
|
|
|
\d{4} # base
|
|
|
)""",
|
|
|
# ASCII Emoticons
|
|
|
EMOTICONS,
|
|
|
# HTML tags:
|
|
|
r"""<[^>\s]+>""",
|
|
|
# ASCII Arrows
|
|
|
r"""[\-]+>|<[\-]+""",
|
|
|
# Twitter username:
|
|
|
r"""(?:@[\w_]+)""",
|
|
|
# Twitter hashtags:
|
|
|
r"""(?:\#+[\w_]+[\w\'_\-]*[\w_]+)""",
|
|
|
# email addresses
|
|
|
r"""[\w.+-]+@[\w-]+\.(?:[\w-]\.?)+[\w-]""",
|
|
|
# docstyle-ignore
|
|
|
# Remaining word types:
|
|
|
r"""
|
|
|
(?:[^\W\d_](?:[^\W\d_]|['\-_])+[^\W\d_]) # Words with apostrophes or dashes.
|
|
|
|
|
|
|
(?:[+\-]?\d+[,/.:-]\d+[+\-]?) # Numbers, including fractions, decimals.
|
|
|
|
|
|
|
(?:[\w_]+) # Words without apostrophes or dashes.
|
|
|
|
|
|
|
(?:\.(?:\s*\.){1,}) # Ellipsis dots.
|
|
|
|
|
|
|
(?:\S) # Everything else that isn't whitespace.
|
|
|
""",
|
|
|
)
|
|
|
|
|
|
######################################################################
|
|
|
# This is the core tokenizing regex:
|
|
|
|
|
|
WORD_RE = regex.compile(r"""(%s)""" % "|".join(REGEXPS), regex.VERBOSE | regex.I | regex.UNICODE)
|
|
|
|
|
|
# WORD_RE performs poorly on these patterns:
|
|
|
HANG_RE = regex.compile(r"([^a-zA-Z0-9])\1{3,}")
|
|
|
|
|
|
# The emoticon string gets its own regex so that we can preserve case for
|
|
|
# them as needed:
|
|
|
EMOTICON_RE = regex.compile(EMOTICONS, regex.VERBOSE | regex.I | regex.UNICODE)
|
|
|
|
|
|
# These are for regularizing HTML entities to Unicode:
|
|
|
ENT_RE = regex.compile(r"&(#?(x?))([^&;\s]+);")
|
|
|
|
|
|
|
|
|
######################################################################
|
|
|
# Functions for converting html entities
|
|
|
######################################################################
|
|
|
|
|
|
|
|
|
def _str_to_unicode(text, encoding=None, errors="strict"):
|
|
|
if encoding is None:
|
|
|
encoding = "utf-8"
|
|
|
if isinstance(text, bytes):
|
|
|
return text.decode(encoding, errors)
|
|
|
return text
|
|
|
|
|
|
|
|
|
def _replace_html_entities(text, keep=(), remove_illegal=True, encoding="utf-8"):
|
|
|
"""
|
|
|
Remove entities from text by converting them to their corresponding unicode character.
|
|
|
|
|
|
Args:
|
|
|
text:
|
|
|
A unicode string or a byte string encoded in the given *encoding* (which defaults to 'utf-8').
|
|
|
keep (list):
|
|
|
List of entity names which should not be replaced. This supports both numeric entities (`&#nnnn;` and
|
|
|
`&#hhhh;`) and named entities (such as ` ` or `>`).
|
|
|
remove_illegal (bool):
|
|
|
If `True`, entities that can't be converted are removed. Otherwise, entities that can't be converted are
|
|
|
kept "as is".
|
|
|
Returns: A unicode string with the entities removed.
|
|
|
|
|
|
See https://github.com/scrapy/w3lib/blob/master/w3lib/html.py
|
|
|
|
|
|
Examples:
|
|
|
|
|
|
```python
|
|
|
>>> from nltk.tokenize.casual import _replace_html_entities
|
|
|
|
|
|
>>> _replace_html_entities(b"Price: £100")
|
|
|
'Price: \\xa3100'
|
|
|
|
|
|
>>> print(_replace_html_entities(b"Price: £100"))
|
|
|
Price: £100
|
|
|
```"""
|
|
|
|
|
|
def _convert_entity(match):
|
|
|
entity_body = match.group(3)
|
|
|
if match.group(1):
|
|
|
try:
|
|
|
if match.group(2):
|
|
|
number = int(entity_body, 16)
|
|
|
else:
|
|
|
number = int(entity_body, 10)
|
|
|
# Numeric character references in the 80-9F range are typically
|
|
|
# interpreted by browsers as representing the characters mapped
|
|
|
# to bytes 80-9F in the Windows-1252 encoding. For more info
|
|
|
# see: https://en.wikipedia.org/wiki/ISO/IEC_8859-1#Similar_character_sets
|
|
|
if 0x80 <= number <= 0x9F:
|
|
|
return bytes((number,)).decode("cp1252")
|
|
|
except ValueError:
|
|
|
number = None
|
|
|
else:
|
|
|
if entity_body in keep:
|
|
|
return match.group(0)
|
|
|
else:
|
|
|
number = html.entities.name2codepoint.get(entity_body)
|
|
|
if number is not None:
|
|
|
try:
|
|
|
return chr(number)
|
|
|
except (ValueError, OverflowError):
|
|
|
pass
|
|
|
|
|
|
return "" if remove_illegal else match.group(0)
|
|
|
|
|
|
return ENT_RE.sub(_convert_entity, _str_to_unicode(text, encoding))
|
|
|
|
|
|
|
|
|
######################################################################
|
|
|
|
|
|
|
|
|
class TweetTokenizer:
|
|
|
r"""
|
|
|
Examples:
|
|
|
|
|
|
```python
|
|
|
>>> # Tokenizer for tweets.
|
|
|
>>> from nltk.tokenize import TweetTokenizer
|
|
|
|
|
|
>>> tknzr = TweetTokenizer()
|
|
|
>>> s0 = "This is a cooool #dummysmiley: :-) :-P <3 and some arrows < > -> <--"
|
|
|
>>> tknzr.tokenize(s0)
|
|
|
['This', 'is', 'a', 'cooool', '#dummysmiley', ':', ':-)', ':-P', '<3', 'and', 'some', 'arrows', '<', '>', '->', '<--']
|
|
|
|
|
|
>>> # Examples using *strip_handles* and *reduce_len parameters*:
|
|
|
>>> tknzr = TweetTokenizer(strip_handles=True, reduce_len=True)
|
|
|
>>> s1 = "@remy: This is waaaaayyyy too much for you!!!!!!"
|
|
|
>>> tknzr.tokenize(s1)
|
|
|
[':', 'This', 'is', 'waaayyy', 'too', 'much', 'for', 'you', '!', '!', '!']
|
|
|
```"""
|
|
|
|
|
|
def __init__(self, preserve_case=True, reduce_len=False, strip_handles=False):
|
|
|
self.preserve_case = preserve_case
|
|
|
self.reduce_len = reduce_len
|
|
|
self.strip_handles = strip_handles
|
|
|
|
|
|
def tokenize(self, text):
|
|
|
"""
|
|
|
Args:
|
|
|
text: str
|
|
|
|
|
|
Returns: list(str) A tokenized list of strings; concatenating this list returns the original string if
|
|
|
`preserve_case=False`
|
|
|
"""
|
|
|
# Fix HTML character entities:
|
|
|
text = _replace_html_entities(text)
|
|
|
# Remove username handles
|
|
|
if self.strip_handles:
|
|
|
text = remove_handles(text)
|
|
|
# Normalize word lengthening
|
|
|
if self.reduce_len:
|
|
|
text = reduce_lengthening(text)
|
|
|
# Shorten problematic sequences of characters
|
|
|
safe_text = HANG_RE.sub(r"\1\1\1", text)
|
|
|
# Tokenize:
|
|
|
words = WORD_RE.findall(safe_text)
|
|
|
# Possibly alter the case, but avoid changing emoticons like :D into :d:
|
|
|
if not self.preserve_case:
|
|
|
words = [x if EMOTICON_RE.search(x) else x.lower() for x in words]
|
|
|
return words
|
|
|
|
|
|
|
|
|
######################################################################
|
|
|
# Normalization Functions
|
|
|
######################################################################
|
|
|
|
|
|
|
|
|
def reduce_lengthening(text):
|
|
|
"""
|
|
|
Replace repeated character sequences of length 3 or greater with sequences of length 3.
|
|
|
"""
|
|
|
pattern = regex.compile(r"(.)\1{2,}")
|
|
|
return pattern.sub(r"\1\1\1", text)
|
|
|
|
|
|
|
|
|
def remove_handles(text):
|
|
|
"""
|
|
|
Remove Twitter username handles from text.
|
|
|
"""
|
|
|
pattern = regex.compile(
|
|
|
r"(?<![A-Za-z0-9_!@#\$%&*])@(([A-Za-z0-9_]){20}(?!@))|(?<![A-Za-z0-9_!@#\$%&*])@(([A-Za-z0-9_]){1,19})(?![A-Za-z0-9_]*@)"
|
|
|
)
|
|
|
# Substitute handles with ' ' to ensure that text on either side of removed handles are tokenized correctly
|
|
|
return pattern.sub(" ", text)
|
|
|
|
|
|
|
|
|
######################################################################
|
|
|
# Tokenization Function
|
|
|
######################################################################
|
|
|
|
|
|
|
|
|
def casual_tokenize(text, preserve_case=True, reduce_len=False, strip_handles=False):
|
|
|
"""
|
|
|
Convenience function for wrapping the tokenizer.
|
|
|
"""
|
|
|
return TweetTokenizer(preserve_case=preserve_case, reduce_len=reduce_len, strip_handles=strip_handles).tokenize(
|
|
|
text
|
|
|
)
|
|
|
|
|
|
|
|
|
###############################################################################
|
|
|
|
|
|
|
|
|
__all__ = ["BertweetTokenizer"]
|
