old_contents: # -*- coding: utf-8 -*-
  """
  nercut 0.1
  
  Dictionary-based maximal matching word segmentation, constrained with
  Thai Character Cluster (TCC) boundaries, and combining tokens that are
  parts of the same named-entity.
  
  Code by Wannaphong Phatthiyaphaibun
  """
  from typing import Iterable, List
  
  from pythainlp.tag.named_entity import ThaiNameTagger
  
  _thainer = ThaiNameTagger()
  
  
  def segment(
      text: str,
      taglist: Iterable[str] = [
          "ORGANIZATION",
          "PERSON",
          "PHONE",
          "EMAIL",
          "DATE",
          "TIME",
      ],
  ) -> List[str]:
      """
      Dictionary-based maximal matching word segmentation, constrained with
      Thai Character Cluster (TCC) boundaries, and combining tokens that are
      parts of the same named-entity.
  
      :param str text: text to be tokenized to words
      :parm list taglist: a list of named-entity tags to be used
      :return: list of words, tokenized from the text
      """
      if not text or not isinstance(text, str):
          return []
  
      global _thainer
      tagged_words = _thainer.get_ner(text, pos=False)
  
      words = []
      combining_word = ""
      combining_word = ""
      for curr_word, curr_tag in tagged_words:
          if curr_tag != "O":
              tag = curr_tag[2:]
          else:
              tag = "O"
  
          if curr_tag.startswith("B-") and tag in taglist:
              if combining_word != "":
                  words.append(combining_word)
              combining_word = curr_word
          elif (
              curr_tag.startswith("I-")
              and combining_word != ""
              and tag in taglist
          ):
              combining_word += curr_word
          elif (
              curr_tag == "O"
              and combining_word != ""
          ):
              words.append(combining_word)
              combining_word = ""
              words.append(curr_word)
          else:
              combining_word = ""
              words.append(curr_word)
  
      if combining_word != "":
          words.append(combining_word)
  
      return words
  
new_contents: # -*- coding: utf-8 -*-
  """
  nercut 0.1
  
  Dictionary-based maximal matching word segmentation, constrained with
  Thai Character Cluster (TCC) boundaries, and combining tokens that are
  parts of the same named-entity.
  
  Code by Wannaphong Phatthiyaphaibun
  """
  from typing import Iterable, List
  
  from pythainlp.tag.named_entity import ThaiNameTagger
  
  _thainer = ThaiNameTagger()
  
  
  def segment(
      text: str,
      taglist: Iterable[str] = [
          "ORGANIZATION",
          "PERSON",
          "PHONE",
          "EMAIL",
          "DATE",
          "TIME",
      ],
  ) -> List[str]:
      """
      Dictionary-based maximal matching word segmentation, constrained with
      Thai Character Cluster (TCC) boundaries, and combining tokens that are
      parts of the same named-entity.
  
      :param str text: text to be tokenized to words
      :parm list taglist: a list of named-entity tags to be used
      :return: list of words, tokenized from the text
      """
      if not text or not isinstance(text, str):
          return []
  
      global _thainer
      tagged_words = _thainer.get_ner(text, pos=False)
  
      words = []
      combining_word = ""
      combining_word = ""
      for curr_word, curr_tag in tagged_words:
          if curr_tag != "O":
              tag = curr_tag[2:]
          else:
              tag = "O"
  
          if curr_tag.startswith("B-") and tag in taglist:
              combining_word = curr_word
          elif (
              curr_tag.startswith("I-")
              and combining_word != ""
              and tag in taglist
          ):
              combining_word += curr_word
          elif (
              curr_tag == "O"
              and combining_word != ""
          ):
              words.append(combining_word)
              combining_word = ""
              words.append(curr_word)
          else:
              combining_word = ""
              words.append(curr_word)
  
      return words
  
subject: Update nercut.py