class_docstr: Simple and fast white-space tokenizer. Splits sentence based on white spaces.
  Punctuation are stripped from tokens.
class_docstr_tok: ["Simple","and","fast","white-space","tokenizer",".","Splits","sentence","based","on","white","spaces",".","Punctuation","are","stripped","from","tokens","."]
code: def __init__(
      self,
      vocab: Iterable[str] = [],
      stop_words: Iterable[str] = ENGLISH_STOP_WORDS,
      do_lower_case: bool = False,
  ):
      self.stop_words = set(stop_words)
      self.do_lower_case = do_lower_case
      self.set_vocab(vocab)
  
file_path: sentence-transformers/sentence_transformers/models/tokenizer/WhitespaceTokenizer.py
imports: from typing import List, Iterable
  import collections
  import string
  import os
  import json
  from .WordTokenizer import WordTokenizer, ENGLISH_STOP_WORDS
label_desc: Use image node_id 1 to create a new WhitespaceTokenizer object from inherited base classes: WordTokenizer with example: obj = WhitespaceTokenizer(vocab, stop_words, do_lower_case)