class_docstr: Augmenter that leverage TF-IDF statistics to insert or substitute word.
  
  :param str model_path: Downloaded model directory. Either model_path or model is must be provided
  :param str action: Either 'insert or 'substitute'. If value is 'insert', a new word will be injected to random
      position according to TF-IDF calculation. If value is 'substitute', word will be replaced according
      to TF-IDF calculation
  :param int top_k: Controlling lucky draw pool. Top k score token will be used for augmentation. Larger k, more
      token can be used. Default value is 5. If value is None which means using all possible tokens.
  :param float aug_p: Percentage of word will be augmented.
  :param int aug_min: Minimum number of word will be augmented.
  :param int aug_max: Maximum number of word will be augmented. If None is passed, number of augmentation is
      calculated via aup_p. If calculated result from aug_p is smaller than aug_max, will use calculated result
      from aug_p. Otherwise, using aug_max.
  :param list stopwords: List of words which will be skipped from augment operation.
  :param str stopwords_regex: Regular expression for matching words which will be skipped from augment operation.
  :param func tokenizer: Customize tokenization process
  :param func reverse_tokenizer: Customize reverse of tokenization process
  :param str name: Name of this augmenter
  
  >>> import nlpaug.augmenter.word as naw
  >>> aug = naw.TfIdfAug(model_path='.')
code: def __init__(
      self,
      model_path=".",
      action=Action.SUBSTITUTE,
      name="TfIdf_Aug",
      aug_min=1,
      aug_max=10,
      aug_p=0.3,
      top_k=5,
      stopwords=None,
      tokenizer=None,
      reverse_tokenizer=None,
      stopwords_regex=None,
      verbose=0,
  ):
      super().__init__(
          action=action,
          name=name,
          aug_p=aug_p,
          aug_min=aug_min,
          aug_max=aug_max,
          stopwords=stopwords,
          tokenizer=tokenizer,
          reverse_tokenizer=reverse_tokenizer,
          device="cpu",
          verbose=verbose,
          stopwords_regex=stopwords_regex,
          include_detail=False,
      )
      self.model_path = model_path
      self.top_k = top_k
      self.model = self.get_model(force_reload=False)
  
file_path: nlpaug/nlpaug/augmenter/word/tfidf.py
imports: from nlpaug.augmenter.word import WordAugmenter
  from nlpaug.util import Action, Doc, WarningException, WarningName, WarningCode, WarningMessage
  import nlpaug.model.word_stats
label_desc: Use image node_id 1 to create a new TfIdfAug object from inherited base classes: WordAugmenter with example: obj = TfIdfAug(model_path, action, name, aug_min, aug_max, aug_p, top_k, stopwords, tokenizer, reverse_tokenizer, stopwords_regex, verbose)