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"]
code: def __init__(
      self,
      data_path,
      sentence_length=None,
      max_word_length=None,
      extract_chars=False,
      lowercase=True,
  ):
      self._validate_paths(data_path)
      self.data_path = data_path
      self.sentence_length = sentence_length
      self.use_chars = extract_chars
      self.max_word_length = max_word_length
      self.lower = lowercase
      self.vocabs = {
          "word": None,
          "char": None,
          "pos": None,
          "chunk": None,
      }
      self._data_dict = {}
  
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 1 to create a new CONLL2000 object from inherited base classes: object with example: obj = CONLL2000(data_path, sentence_length, max_word_length, extract_chars, lowercase)