input: Write a function to develop a machine learning model in Python to identify the author of a given article. The function should use a pipeline that includes TfidfVectorizer and GaussianNB. The input to the function should be a list of article samples and their corresponding labels. The function should return the score of the model.
  assert author_identification(["sample 1 from AuthorA", "sample 2 from AuthorA", "sample 1 from AuthorB", "sample 2 from AuthorB"], ["AuthorA", "AuthorA", "AuthorB", "AuthorB"]) is not None
  assert author_identification(["sample 1 from AuthorC", "sample 2 from AuthorC", "sample 1 from AuthorD", "sample 2 from AuthorD"], ["AuthorC", "AuthorC", "AuthorD", "AuthorD"]) is not None
  assert author_identification(["sample 1 from AuthorE"], ["AuthorE"]) is not None
  assert author_identification([], []) is None
output: def author_identification(articles, labels):
      if not articles or not labels:
          return None
      
      from sklearn.feature_extraction.text import TfidfVectorizer
      from sklearn.naive_bayes import GaussianNB
      from sklearn.pipeline import make_pipeline
      from sklearn.model_selection import cross_val_score
      
      pipeline = make_pipeline(TfidfVectorizer(), GaussianNB())
      scores = cross_val_score(pipeline, articles, labels, cv=3)
      return scores.mean()