class_docstr: Instantiate a fastText classifier that is compatible with :py:class:`CleanLearning <cleanlab.classification.CleanLearning>`.
  
  Parameters
  ----------
  train_data_fn: str
      File name of the training data in the format compatible with fastText.
  
  test_data_fn: str, optional
      File name of the test data in the format compatible with fastText.
class_docstr_tok: ["Instantiate","a","fastText","classifier","that","is","compatible","with",":","py",":","class",":","`","CleanLearning","<","cleanlab.classification.CleanLearning",">","`",".","Parameters","--","--","--","--","--","train_data_fn",":","str","File","name","of","the","training","data","in","the","format","compatible","with","fastText",".","test_data_fn",":","str",",","optional","File","name","of","the","test","data","in","the","format","compatible","with","fastText","."]
docstr: Deletes intermediate data files.
docstr_tok: ["Deletes","intermediate","data","files","."]
code: def _remove_masked_data(self, fn):
      """Deletes intermediate data files."""
  
      if self.del_intermediate_data and self.masked_data_was_created:
          os.remove(fn)
  
file_path: cleanlab/cleanlab/models/fasttext.py
imports: import time
  import os
  import copy
  import numpy
  from sklearn.base import BaseEstimator
  from fasttext import train_supervised, load_model
label_desc: Use image node_id 3 for calling the FastTextClassifier obj's underlying member method code with example usage: obj._remove_masked_data(fn) without return types