input: Write a function to develop a machine learning model in Python to identify the author of a given article. The function should return the model score after evaluating it on a test set. If the sample data or labels are empty, the function should return None.
  assert author_identification(["sample 1 from AuthorA", "sample 2 from AuthorA", "sample 1 from AuthorB", "sample 2 from AuthorB"], ["AuthorA", "AuthorA", "AuthorB", "AuthorB"])==0.5
  assert author_identification(["sample 1 from AuthorA"], ["AuthorA"])==None
  assert author_identification([], [])==None
output: def author_identification(samples, labels):
      if not samples or not labels:
          return None
      
      from sklearn.feature_extraction.text import TfidfVectorizer
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.model_selection import train_test_split
      from sklearn.metrics import accuracy_score
      
      vectorizer = TfidfVectorizer()
      X = vectorizer.fit_transform(samples)
      X_train, X_test, y_train, y_test = train_test_split(X, labels, test_size=0.5, random_state=42)
      
      model = MultinomialNB()
      model.fit(X_train, y_train)
      
      y_pred = model.predict(X_test)
      return accuracy_score(y_test, y_pred)