original_docstring: 
      Retrieve the corpus and the labels
  
      Returns
      -------
      result : dictionary with corpus, training and test partition
               and labels of the corpus
      
docstring: Retrieve the corpus and the labels
  Returns
  
  result : dictionary with corpus, training and test partition
  and labels of the corpus
docstring_tokens: Retrieve the corpus and the labels Returns result : dictionary with corpus training and test partition and labels of the corpus
code: def retrieve_20newsgroup_scikit():
      """
      Retrieve the corpus and the labels
  
      Returns
      -------
      result : dictionary with corpus, training and test partition
               and labels of the corpus
      """
      corpus = []
      labels = []
  
      newsgroup = fetch_20newsgroups(remove=('headers', 'footers', 'quotes'))
  
      cate = newsgroup.target_names
  
      corpus = corpus + newsgroup.data
  
      for doc in newsgroup.target:
          labels.append([cate[doc]])
  
      partition = len(corpus) - 1
  
      result = dict()
      result["partition"] = partition
      result["corpus"] = corpus
      result["doc_labels"] = labels
      result["info"] = {
          "name": "20-newsgroups",
          "link": "http://qwone.com/~jason/20Newsgroups/20news-18828.tar.gz",
          "source": "https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html",
          "num_records": 18846,
          "description": "The notorious collection of approximately 20,000 newsgroup posts, partitioned (nearly) evenly across 20 different newsgroups.",
          "file_name": "20-newsgroups.gz",
          "info": "http://qwone.com/~jason/20Newsgroups/"
      }
  
      return result
code_tokens: def retrieve_20newsgroup_scikit ( ) : corpus = [ ] labels = [ ] newsgroup = fetch_20newsgroups ( remove = ( 'headers' , 'footers' , 'quotes' ) ) cate = newsgroup . target_names corpus = corpus + newsgroup . data for doc in newsgroup . target : labels . append ( [ cate [ doc ] ] ) partition = len ( corpus ) - 1 result = dict ( ) result [ "partition" ] = partition result [ "corpus" ] = corpus result [ "doc_labels" ] = labels result [ "info" ] = { "name" : "20-newsgroups" , "link" : "http://qwone.com/~jason/20Newsgroups/20news-18828.tar.gz" , "source" : "https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html" , "num_records" : 18846 , "description" : "The notorious collection of approximately 20,000 newsgroup posts, partitioned (nearly) evenly across 20 different newsgroups." , "file_name" : "20-newsgroups.gz" , "info" : "http://qwone.com/~jason/20Newsgroups/" } return result
short_docstring: Retrieve the corpus and the labels
  Returns
short_docstring_tokens: Retrieve the corpus and the labels Returns
comment: """
      Retrieve the corpus and the labels
  
      Returns
      -------
      result : dictionary with corpus, training and test partition
               and labels of the corpus
      """