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 _gen_data(self):
      train, test = self._load_data()
      train_size = len(train)
      test_size = len(test)
      sentences = self._extract(train, test, 0)
      pos_tags = self._extract(train, test, 1)
      chunk_tags = self._extract(train, test, 2)
      sentence_vecs, word_vocab = word_vector_generator(
          sentences, self.lower, 2
      )
      pos_vecs, pos_vocab = word_vector_generator(pos_tags, start=1)
      chunk_vecs, chunk_vocab = word_vector_generator(
          chunk_tags, start=1
      )
      self.vocabs = {
          "word": word_vocab,  # 0=pad, 1=unk
          "pos": pos_vocab,  # 0=pad, 1=unk
          "chunk": chunk_vocab,
      }  # 0=pad
      if self.sentence_length is not None:
          sentence_vecs = pad_sentences(
              sentence_vecs, max_length=self.sentence_length
          )
          chunk_vecs = pad_sentences(
              chunk_vecs, max_length=self.sentence_length
          )
          pos_vecs = pad_sentences(
              pos_vecs, max_length=self.sentence_length
          )
      self._data_dict["train"] = (
          sentence_vecs[:train_size],
          pos_vecs[:train_size],
          chunk_vecs[:train_size],
      )
      self._data_dict["test"] = (
          sentence_vecs[-test_size:],
          pos_vecs[-test_size:],
          chunk_vecs[-test_size:],
      )
      if self.use_chars:
          chars_vecs, char_vocab = character_vector_generator(
              sentences, start=2
          )
          self.vocabs.update({"char": char_vocab})  # 0=pad, 1=unk
          if self.max_word_length is not None:
              chars_vecs = [
                  pad_sentences(d, max_length=self.max_word_length)
                  for d in chars_vecs
              ]
              zeros = np.zeros(
                  (
                      len(chars_vecs),
                      self.sentence_length,
                      self.max_word_length,
                  )
              )
              for idx, d in enumerate(chars_vecs):
                  d = d[: self.sentence_length]
                  zeros[idx, -d.shape[0] :] = d
              chars_vecs = zeros.astype(dtype=np.int32)
          self._data_dict["train"] += (chars_vecs[:train_size],)
          self._data_dict["test"] += (chars_vecs[-test_size:],)
  
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 11 for calling the CONLL2000 obj's underlying member method code with example usage: obj._gen_data() without return types