class_docstr: CONLL 2000 POS/chunking task data set (numpy)
  
  Arguments:
      data_path (str): directory containing CONLL2000 files
      sentence_length (int, optional): number of time steps to embed the data.
          None value will not truncate vectors
      max_word_length (int, optional): max word length in characters.
          None value will not truncate vectors
      extract_chars (boolean, optional): Yield Char RNN features.
      lowercase (bool, optional): lower case sentence words
class_docstr_tok: ["CONLL","2000","POS\/chunking","task","data","set","(","numpy",")","Arguments",":","data_path","(","str",")",":","directory","containing","CONLL2000","files","sentence_length","(","int",",","optional",")",":","number","of","time","steps","to","embed","the","data",".","None","value","will","not","truncate","vectors","max_word_length","(","int",",","optional",")",":","max","word","length","in","characters",".","None","value","will","not","truncate","vectors","extract_chars","(","boolean",",","optional",")",":","Yield","Char","RNN","features",".","lowercase","(","bool",",","optional",")",":","lower","case","sentence","words"]
docstr: open files and parse
  return format: list of 3-tuples (word list, POS list, chunk list)
docstr_tok: ["open","files","and","parse","return","format",":","list","of","3-tuples","(","word","list",",","POS","list",",","chunk","list",")"]
code: def _load_data(self):
      """
      open files and parse
      return format: list of 3-tuples (word list, POS list, chunk list)
      """
      train_set = read_sequential_tagging_file(
          self.dataset_files["train"]
      )
      test_set = read_sequential_tagging_file(
          self.dataset_files["test"]
      )
      train_data = [list(zip(*x)) for x in train_set]
      test_data = [list(zip(*x)) for x in test_set]
      return train_data, test_data
  
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 3 for calling the CONLL2000 obj's underlying member method code with example usage: obj._load_data() and returns: train_data,  test_data