input: def identify_article_author(samples: List[str], labels: List[str]) -> float:
      """Develop a machine learning model in Python to identify the author of a given article.
      
      >>> identify_article_author([
      ...     "sample 1 from AuthorA",
      ...     "sample 2 from AuthorA",
      ...     "sample 1 from AuthorB",
      ...     "sample 2 from AuthorB"
      ... ], [
      ...     "AuthorA",
      ...     "AuthorA",
      ...     "AuthorB",
      ...     "AuthorB"
      ... ])
      model score: 1.0
      """
output:     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
  
      # Vectorize the text data
      vectorizer = TfidfVectorizer()
      X = vectorizer.fit_transform(samples)
  
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, labels, test_size=0.2, random_state=42)
  
      # Train a Naive Bayes classifier
      model = MultinomialNB()
      model.fit(X_train, y_train)
  
      # Predict on the test set
      y_pred = model.predict(X_test)
  
      # Calculate the accuracy score
      score = accuracy_score(y_test, y_pred)
  
      print(f"model score: {score}")
      return score