class_docstr: Chinese Daily New New Corpus
  https://github.com/zjy-ucas/ChineseNER/
  
  Example:
      >>> from kashgari.corpus import ChineseDailyNerCorpus
      >>> train_x, train_y = ChineseDailyNerCorpus.load_data('train')
      >>> test_x, test_y = ChineseDailyNerCorpus.load_data('test')
      >>> valid_x, valid_y = ChineseDailyNerCorpus.load_data('valid')
      >>> print(train_x)
          [['海', '钓', '比', '赛', '地', '点', '在', '厦', '门', ...], ...]
      >>> print(train_y)
          [['O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-LOC', 'I-LOC', ...], ...]
class_docstr_tok: ["Chinese","Daily","New","New","Corpus","https",":","\/\/github.com\/zjy-ucas\/ChineseNER\/","Example",":",">",">",">","from","kashgari.corpus","import","ChineseDailyNerCorpus",">",">",">","train_x",",","train_y","=","ChineseDailyNerCorpus.load_data","(","'train","'",")",">",">",">","test_x",",","test_y","=","ChineseDailyNerCorpus.load_data","(","'test","'",")",">",">",">","valid_x",",","valid_y","=","ChineseDailyNerCorpus.load_data","(","'valid","'",")",">",">",">","print","(","train_x",")","[","[","'","\u6d77","'",",","'","\u9493","'",",","'","\u6bd4","'",",","'","\u8d5b","'",",","'","\u5730","'",",","'","\u70b9","'",",","'","\u5728","'",",","'","\u53a6","'",",","'","\u95e8","'",",","...","]",",","...","]",">",">",">","print","(","train_y",")","[","[","'","O","'",",","'","O","'",",","'","O","'",",","'","O","'",",","'","O","'",",","'","O","'",",","'","O","'",",","'","B-LOC","'",",","'","I-LOC","'",",","...","]",",","...","]"]
docstr: Load dataset as sequence labeling format, char level tokenized
  
  Args:
      subset_name: {train, test, valid}
      shuffle: should shuffle or not, default True.
  
  Returns:
      dataset_features and dataset labels
docstr_tok: ["Load","dataset","as","sequence","labeling","format",",","char","level","tokenized","Args",":","subset_name",":","{","train",",","test",",","valid","}","shuffle",":","should","shuffle","or","not",",","default","True",".","Returns",":","dataset_features","and","dataset","labels"]
code: def load_data(
      cls, subset_name: str = "train", shuffle: bool = True
  ) -> Tuple[List[List[str]], List[List[str]]]:
      """
      Load dataset as sequence labeling format, char level tokenized
  
      Args:
          subset_name: {train, test, valid}
          shuffle: should shuffle or not, default True.
  
      Returns:
          dataset_features and dataset labels
      """
      corpus_path = get_file(
          cls.__corpus_name__,
          cls.__zip_file__name,
          cache_dir=K.DATA_PATH,
          untar=True,
      )
  
      if subset_name == "train":
          file_path = os.path.join(corpus_path, "example.train")
      elif subset_name == "test":
          file_path = os.path.join(corpus_path, "example.test")
      else:
          file_path = os.path.join(corpus_path, "example.dev")
  
      x_data, y_data = DataReader.read_conll_format_file(file_path)
      if shuffle:
          x_data, y_data = utils.unison_shuffled_copies(x_data, y_data)
      logger.debug(
          f"loaded {len(x_data)} samples from {file_path}. Sample:\n"
          f"x[0]: {x_data[0]}\n"
          f"y[0]: {y_data[0]}"
      )
      return x_data, y_data
  
imports: import os
  from typing import List
  from typing import Tuple
  import numpy
  import pandas
  from tensorflow.keras.utils import get_file
  from kashgari import macros
  from kashgari import utils
  from kashgari.logger import logger
  from kashgari.tokenizers.base_tokenizer import Tokenizer
  from kashgari.tokenizers.bert_tokenizer import BertTokenizer
label_desc: Use image node_id 1 for calling the ChineseDailyNerCorpus obj's underlying member method code with example usage: obj.load_data(cls, subset_name, shuffle) and returns: x_data,  y_data