input: Write a python function to load datasets, encode labels, vectorize text, split the data, train a model, and evaluate it using Naive Bayes. 
  assert model_evaluation() == "expected_classification_report"
  assert model_evaluation() is not None
  assert isinstance(model_evaluation(), str)
output: def model_evaluation():
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.preprocessing import LabelEncoder
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.metrics import classification_report
  
      # Load datasets (assuming CSV files with 'text' and 'label' columns)
      df = pd.read_csv('dataset.csv')
      
      # Encode labels
      le = LabelEncoder()
      df['label'] = le.fit_transform(df['label'])
      
      # Vectorize text
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(df['text'])
      
      # Split the data
      X_train, X_test, y_train, y_test = train_test_split(X, df['label'], test_size=0.2, random_state=42)
      
      # Train a model
      model = MultinomialNB()
      model.fit(X_train, y_train)
      
      # Evaluate the model
      y_pred = model.predict(X_test)
      report = classification_report(y_test, y_pred)
      
      return report