input: 
  from sklearn.feature_extraction.text import TfidfVectorizer
  from sklearn.naive_bayes import GaussianNB
  from sklearn.pipeline import Pipeline
  from sklearn.model_selection import train_test_split
  
  def author_identification(samples, labels):
      """Given a list of text samples and their corresponding authors, this function
      trains a machine learning model to identify the author of a given article.
      
      >>> samples = ["sample 1 from AuthorA", "sample 2 from AuthorA", "sample 1 from AuthorB", "sample 2 from AuthorB"]
      >>> labels = ["AuthorA", "AuthorA", "AuthorB", "AuthorB"]
      >>> author_identification(samples, labels)
      model score: 0.5
      """
      # build the model
      model = Pipeline([
        ('tfidf', TfidfVectorizer()),
        ('clf', GaussianNB())
      ])
  
      # train the model
      X_train, X_test, y_train, y_test = train_test_split(samples, labels, test_size=0.3)
      model.fit(X_train, y_train)
  
      # evalulate the model
      score = model.score(X_test, y_test)
      print(f'model score: {score}')
  
output:     return score