old_contents: """
  Example of how to use the BERTTrainer class.
  """
  
  from bert_trainer import BERTTrainer
  
  def get_filtered_nyt_data(data_path):
      print('Reading data...')
      df = pd.read_pickle(data_path)
      sections = df[['section', 'desk']].drop_duplicates()
      category_counts = sections.groupby('section').count().sort_values('desk', ascending=False)
      big_category_df = category_counts[category_counts['desk'] >= 20]
  
      big_categories = list(big_category_df.index)
  
      filtered = df[df['section'].isin(big_categories)]
      return filtered
  
  if __name__ == '__main__':
  	# Read in data. No need to separate training and validation,
  	# BERTTrainer class takes care of that.
  	nyt_data = get_filtered_nyt_data('nyt_data_from_2015.pkl')
      trainer = BERTTrainer(nyt_data)
      trainer.train_model()
      eval_accuracy_info = trainer.test_model()
      prin(eval_accuracy_info)
      preds = trainer.predict([
          "Donald Trump",
          "20th century art",
          "Donald Trump likes 20th century art"
      ])
      print(preds)
      # can use these predicted values to compute various other eval metrics 
  
new_contents: """
  Example of how to use the BERTTrainer class.
  """
  
  from bert_trainer import BERTTrainer
  
  def get_filtered_nyt_data(data_path):
      print('Reading data...')
      df = pd.read_pickle(data_path)
      sections = df[['section', 'desk']].drop_duplicates()
      category_counts = sections.groupby('section').count().sort_values('desk', ascending=False)
      big_category_df = category_counts[category_counts['desk'] >= 20]
  
      big_categories = list(big_category_df.index)
  
      filtered = df[df['section'].isin(big_categories)]
      return filtered
  
  if __name__ == '__main__':
      # Read in data. No need to separate training and validation,
      # BERTTrainer class takes care of that.
      nyt_data = get_filtered_nyt_data('nyt_data_from_2015.pkl')
      trainer = BERTTrainer(nyt_data)
      trainer.train_model()
      eval_accuracy_info = trainer.test_model()
      print(eval_accuracy_info)
      preds = trainer.predict([
          "Donald Trump",
          "20th century art",
          "Donald Trump likes 20th century art"
      ])
      print(preds)
  
      # can use these predicted values to compute various other eval metrics 
  
subject: Fix indentation issues