new_contents: import os
  import re
  import unicodedata
  
  from libshorttext.converter import *
  
  
  def tokenizer(text):
  
      def trim_links(input_text):
          input_text = re.sub(r' (http|www|t\.co).*?(?: |$)', '', input_text)
          return input_text
  
      def trim_char(c):
          if ord(c) > 127:
              return ''
          if c.isdigit() or c.isalpha() or c.isspace():
              return c
          if re.match(r'[#$/\-_]', c):
              return c
          else:
              return ''
  
      def trim_hashtags(input_text):
          tag = re.search(r'#+([\w_]+[\w\'_\-]*[\w_]+)', input_text)
          if tag:
              hashtag = tag.group(0)
              tag_text = tag.group(1)
              splittable = re.match(r'.*([A-Z]|_|-|\d)', tag_text)
              if splittable:
                  tokens = [token for token in re.split(r'([A-Z][a-z]*)|_|-|(\d*)', tag_text) if token]
                  new_text = ' '.join(tokens)
                  input_text = input_text.replace(hashtag, new_text)
                  return trim_hashtags(input_text)
              else:
                  input_text = input_text.replace(hashtag, tag_text)
                  return trim_hashtags(input_text)
          else:
              return input_text
  
      def trim_digits(input_text):
          return re.sub(r'\d+', r'#', input_text)
  
      def trim_spaces(input_text):
          return re.sub(r'\s+', r' ', input_text)
  
      text = unicodedata.normalize('NFD', unicode(text, 'utf-8'))
      text = trim_links(text)
      text = ''.join(map(trim_char, text))
      text = trim_hashtags(text)
      text = re.sub(r'([a-z])([0-9])', r'\1 \2', text)
      text = re.sub(r'([0-9])([a-z])', r'\1 \2', text)
      text = trim_digits(text)
      text = trim_spaces(text)
      return text.strip().lower().split()
  
  text_converter = Text2svmConverter('-stopword 1 -stemming 1')
  text_converter.text_prep.tokenizer = tokenizer
  
  
  def process():
      os.chdir(os.path.dirname(__file__))
      convert_text('training_data/post_sale,event,food,info', text_converter, 'outputs/test1.svm')
      text_converter.save('outputs/test1.text_converter')
  
  process()
  
subject: Add custom text2svm preprocessor with a blvd-minded tokenizer.