class_docstr: Compressed size of first 10^9 bytes of enwiki-20060303-pages-articles.xml.
  It's part of Large Text Compression Benchmark project
docstr: Initiate EnWik9 dataset.
  
  Args:
      begin_line: the number of beginning line. Default: 0
      num_lines: the number of lines to be loaded. Default: 6348957
      root: Directory where the datasets are saved. Default: ".data"
      data: a list of label/tokens tuple. tokens are a tensor after
  
  Examples:
      >>> from torchtext.datasets import EnWik9
      >>> enwik9 = EnWik9(num_lines=20000)
      >>> vocab = enwik9.get_vocab()
docstr_tok: ["Initiate","EnWik9","dataset",".","Args",":","begin_line",":","the","number","of","beginning","line",".","Default",":","0","num_lines",":","the","number","of","lines","to","be","loaded",".","Default",":","6348957","root",":","Directory","where","the","datasets","are","saved",".","Default",":","``",".data","''","data",":","a","list","of","label\/tokens","tuple",".","tokens","are","a","tensor","after","Examples",":",">",">",">","from","torchtext.datasets","import","EnWik9",">",">",">","enwik9","=","EnWik9","(","num_lines=20000",")",">",">",">","vocab","=","enwik9.get_vocab","(",")"]
code: def __init__(self, begin_line=0, num_lines=6348957, root=".data"):
      """Initiate EnWik9 dataset.
  
      Args:
          begin_line: the number of beginning line. Default: 0
          num_lines: the number of lines to be loaded. Default: 6348957
          root: Directory where the datasets are saved. Default: ".data"
          data: a list of label/tokens tuple. tokens are a tensor after
  
      Examples:
          >>> from torchtext.datasets import EnWik9
          >>> enwik9 = EnWik9(num_lines=20000)
          >>> vocab = enwik9.get_vocab()
      """
  
      super(EnWik9, self).__init__()
  
      processed_file = os.path.join(root, "norm_enwik9")
      if not os.path.exists(processed_file):
          url = "http://mattmahoney.net/dc/enwik9.zip"
          dataset_zip = download_from_url(
              url, path=os.path.join(root, "enwik9.zip"), root=root
          )
          extracted_file = extract_archive(dataset_zip)
          raw_file = extracted_file[0]
          preprocess_raw_enwik9(raw_file, processed_file)
  
      # Meta information
      offsets = generate_offsets(processed_file)
      read_lines = read_lines_from_iterator(
          processed_file, offsets, begin_line, num_lines
      )
  
      self._data = []
      for item in simple_space_split(read_lines):
          self._data += item
  
      self._vocab = None
  
file_path: pytext/pytext/legacy/datasets/unsupervised_learning.py
imports: import os
  import torch
  from torchtext.data.functional import custom_replace, simple_space_split
  from torchtext.utils import download_from_url, extract_archive
  from ..vocab import build_vocab_from_iterator
label_desc: Use image node_id 1 to create a new EnWik9 object from inherited base classes: torch.utils.data with example: obj = EnWik9(begin_line, num_lines, root)