input: 
  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 build_author_identification_model(samples: List[str], labels: List[str]) -> float:
      """ Builds and evaluates a machine learning model to identify the author of a given article.
      The model uses a pipeline with TF-IDF vectorizer and Gaussian Naive Bayes classifier.
      >>> build_author_identification_model([
      ...   "sample 1 from AuthorA",
      ...   "sample 2 from AuthorA",
      ...   "sample 1 from AuthorB",
      ...   "sample 2 from AuthorB"
      ... ], [
      ...   "AuthorA",
      ...   "AuthorA",
      ...   "AuthorB",
      ...   "AuthorB"
      ... ])
      1.0
      """
  
      # 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)
      print(f'model score: {score}')
      return score
  
output:     # 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)
      print(f'model score: {score}')
      return score