input: Write a function to develop a machine learning model in Python that identifies the author of a given article using Naive Bayes classifier and TF-IDF vectorization. The function should accept a list of article samples and their corresponding labels, train the model on a split of the data, and return the model score on the test set. If the input list is empty, the function should return a message indicating that there is no data to train the model.
  assert identify_author(["sample 1 from AuthorA", "sample 2 from AuthorA", "sample 1 from AuthorB", "sample 2 from AuthorB"], ["AuthorA", "AuthorA", "AuthorB", "AuthorB"]) == 1.0
  assert identify_author([], []) == "No data to train the model."
output: def identify_author(articles, labels):
      if not articles:
          return "No data to train the model."
      
      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
      
      X_train, X_test, y_train, y_test = train_test_split(articles, labels, test_size=0.2, random_state=42)
      
      vectorizer = TfidfVectorizer()
      X_train_tfidf = vectorizer.fit_transform(X_train)
      X_test_tfidf = vectorizer.transform(X_test)
      
      model = MultinomialNB()
      model.fit(X_train_tfidf, y_train)
      
      y_pred = model.predict(X_test_tfidf)
      return accuracy_score(y_test, y_pred)