input: Write a function to develop a machine learning model in Python to identify the author of a given article. The function should use the TfidfVectorizer for text feature extraction and Gaussian Naive Bayes for classification. The function should split the data into training and testing sets, train the model, and then evaluate its accuracy on the test set.
  
  assert identify_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: def identify_author(articles, authors):
      from sklearn.feature_extraction.text import TfidfVectorizer
      from sklearn.naive_bayes import GaussianNB
      from sklearn.model_selection import train_test_split
      from sklearn.preprocessing import LabelEncoder
      from sklearn.metrics import accuracy_score
  
      vectorizer = TfidfVectorizer()
      X = vectorizer.fit_transform(articles).toarray()
      le = LabelEncoder()
      y = le.fit_transform(authors)
  
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      model = GaussianNB()
      model.fit(X_train, y_train)
  
      y_pred = model.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
  
      return f"model score: {accuracy}"