docstr: Finer segmentation for search engines.
  Args:
      sentence(str): The str(unicode) to be segmented.
      HMM(bool): Whether to use the Hidden Markov Model.
  
  Returns:
      results(dict): The word segmentation result of the input sentence, whose key is 'word'.
docstr_tok: ["Finer","segmentation","for","search","engines",".","Args",":","sentence","(","str",")",":","The","str","(","unicode",")","to","be","segmented",".","HMM","(","bool",")",":","Whether","to","use","the","Hidden","Markov","Model",".","Returns",":","results","(","dict",")",":","The","word","segmentation","result","of","the","input","sentence",",","whose","key","is","'word","'","."]
code: def cut_for_search(self, sentence, HMM=True):
      """
      Finer segmentation for search engines.
      Args:
          sentence(str): The str(unicode) to be segmented.
          HMM(bool): Whether to use the Hidden Markov Model.
  
      Returns:
          results(dict): The word segmentation result of the input sentence, whose key is 'word'.
      """
      self.check_dependency()
      import jieba
  
      jieba.setLogLevel(logging.ERROR)
      res = " ".join(jieba.cut_for_search(sentence, HMM=HMM))
      seg_list = res.strip(" ").split(" ")
      return seg_list
  
file_path: PaddleHub/modules/text/lexical_analysis/jieba_paddle/module.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import logging
  import os
  import paddlehub
  from paddlehub.common.logger import logger
  from paddlehub.module.module import moduleinfo
  from paddlehub.module.module import serving
label_desc: Use image node_id 5 for calling the JiebaPaddle obj's underlying member method code with example usage: obj.cut_for_search(sentence, HMM) and returns: seg_list