class_docstr: NER preprocessing base class
class_docstr_tok: ["NER","preprocessing","base","class"]
docstr: Args:
    x_test(list of lists of str): lists of token lists
    x_test (list of lists of str):  lists of tag lists
    verbose(bool): verbosity
  Returns:
    NERSequence:  can be used as argument to NERLearner.validate() to evaluate test sets
docstr_tok: ["Args",":","x_test","(","list","of","lists","of","str",")",":","lists","of","token","lists","x_test","(","list","of","lists","of","str",")",":","lists","of","tag","lists","verbose","(","bool",")",":","verbosity","Returns",":","NERSequence",":","can","be","used","as","argument","to","NERLearner.validate","(",")","to","evaluate","test","sets"]
code: def preprocess_test(self, x_test, y_test, verbose=1):
      """
      Args:
        x_test(list of lists of str): lists of token lists
        x_test (list of lists of str):  lists of tag lists
        verbose(bool): verbosity
      Returns:
        NERSequence:  can be used as argument to NERLearner.validate() to evaluate test sets
      """
      # array > df > array in order to print statistics more easily
      from .data import array_to_df
  
      test_df = array_to_df(x_test, y_test)
      (x_list, y_list) = process_df(test_df, verbose=verbose)
      from .dataset import NERSequence
  
      return NERSequence(
          x_list, y_list, batch_size=U.DEFAULT_BS, p=self.p
      )
  
imports: from ...None import utils
  from ...imports import *
  from ...preprocessor import Preprocessor
  from ..None import preprocessor
  from ..None import textutils
label_desc: Use image node_id 7 for calling the NERPreprocessor obj's underlying member method code with example usage: obj.preprocess_test(x_test, y_test, verbose) and returns: NERSequence