input: 
  from typing import List
  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 identify_author(samples: List[str], labels: List[str]) -> float:
      """
      Develops and trains a machine learning model to identify the author of given articles.
      Returns the accuracy score of the model on the test data.
      >>> identify_author(["sample 1 from AuthorA", "sample 2 from AuthorA", "sample 1 from AuthorB", "sample 2 from AuthorB"],
                         ["AuthorA", "AuthorA", "AuthorB", "AuthorB"])
      0.0 # Assuming no match in this example for brevity.
      """
      # 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)
  
      # evaluate the model
      score = model.score(X_test, y_test)
      return score
  
output:     
      return score
  