class_docstr: Tokenizes the text with respect to existent phrases in the vocab.
  
  This tokenizers respects phrases that are in the vocab. Phrases are separated with 'ngram_separator', for example,
  in Google News word2vec file, ngrams are separated with a _ like New_York. These phrases are detected in text and merged as one special token. (New York is the ... => [New_York, is, the])
code: def __init__(
      self,
      vocab: Iterable[str] = [],
      stop_words: Iterable[str] = ENGLISH_STOP_WORDS,
      do_lower_case: bool = False,
      ngram_separator: str = "_",
      max_ngram_length: int = 5,
  ):
      self.stop_words = set(stop_words)
      self.do_lower_case = do_lower_case
      self.ngram_separator = ngram_separator
      self.max_ngram_length = max_ngram_length
      self.set_vocab(vocab)
  
file_path: sentence-transformers/sentence_transformers/models/tokenizer/PhraseTokenizer.py
imports: from typing import List, Iterable
  import collections
  import string
  import os
  import json
  import logging
  from .WordTokenizer import WordTokenizer, ENGLISH_STOP_WORDS
  import nltk
label_desc: Use image node_id 1 to create a new PhraseTokenizer object from inherited base classes: WordTokenizer with example: obj = PhraseTokenizer(vocab, stop_words, do_lower_case, ngram_separator, max_ngram_length)