class_docstr: Sequence token classification Processor dataset loader.
  Loads a directory with train.txt/test.txt/dev.txt files in tab separeted
  format (one token per line - conll style).
  Label dictionary is given in labels.txt file.
class_docstr_tok: ["Sequence","token","classification","Processor","dataset","loader",".","Loads","a","directory","with","train.txt\/test.txt\/dev.txt","files","in","tab","separeted","format","(","one","token","per","line","-","conll","style",")",".","Label","dictionary","is","given","in","labels.txt","file","."]
docstr: See base class.
docstr_tok: ["See","base","class","."]
code: def _create_examples(cls, lines, set_type):
      """See base class."""
      examples = []
      for i, (sentence, labels) in enumerate(lines):
          guid = "%s-%s" % (set_type, i)
          text = " ".join(sentence)
          shapes = [cls._get_shape(w) for w in sentence]
          examples.append(
              TokenClsInputExample(
                  guid=guid,
                  text=text,
                  tokens=sentence,
                  label=labels,
                  shapes=shapes,
              )
          )
      return examples
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import os
  from os import path
  from typing import List
  import numpy
  from nlp_architect.data.utils import DataProcessor, InputExample, read_column_tagged_file
  from nlp_architect.utils.generic import pad_sentences
  from nlp_architect.utils.io import validate_existing_directory, validate_existing_filepath
  from nlp_architect.utils.text import character_vector_generator, read_sequential_tagging_file, word_vector_generator
  from nlp_architect.utils.text import Vocabulary
label_desc: Use image node_id 9 for calling the TokenClsProcessor obj's underlying member method code with example usage: obj._create_examples(cls, lines, set_type) and returns: examples