text: import joblib
  import sklearn
  from sklearn.datasets import fetch_20newsgroups
  from sklearn.feature_extraction.text import TfidfVectorizer
  from sklearn.naive_bayes import MultinomialNB
  from sklearn.pipeline import make_pipeline
  
  # load some categories of newsgroups dataset
  categories = [
      "soc.religion.christian",
      "talk.religion.misc",
      "sci.space",
      "comp.sys.mac.hardware",
      'sci.crypt',
      'sci.electronics',
      'sci.med',
      'talk.politics.guns',
      'talk.politics.mideast',
      'comp.graphics',
      
  ]
  
  newsgropus_training = fetch_20newsgroups(
      subset="train", categories=categories, shuffle=True, random_state=0
  )
  newsgropus_testing = fetch_20newsgroups(
      subset="test", categories=categories, shuffle=True, random_state=0
  )
  
  # make the pipeline
  
  model = make_pipeline(
      TfidfVectorizer(),
      MultinomialNB(),
      )
  
  # train the model
  model.fit(newsgropus_training.data, newsgropus_training.target)
  
  
  # Serielize the model and the target names
  model_file = "newsgroups_model.joblib"
  model_targets_tuple = (model, newsgropus_training.target_names)
  joblib.dump(model_targets_tuple, model_file)