input: from typing import List, Tuple
  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 Author_Identification_Model(samples: List[str], labels: List[str]) -> float:
      """ Develop a machine learning model to identify the author of a given article using TF-IDF Vectorization and Gaussian Naive Bayes.
      The function returns the score of the evaluation of the model.
      >>> Author_Identification_Model(["sample 1 from AuthorA", "sample 2 from AuthorA", "sample 1 from AuthorB", "sample 2 from AuthorB"], ["AuthorA", "AuthorA", "AuthorB", "AuthorB"])
      0.5
      """
      
      # 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)
      
      # evalulate the model
      score = model.score(X_test, y_test)
      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)
      
      # evalulate the model
      score = model.score(X_test, y_test)
      return score