old_contents: """Main entry point for any experiments."""
  
  import os
  import sys
  import dataset.reuters21578.parser as data
  import nlp.utils as nlp
  from evaluation.mlc import evaluate
  from model.binary_classifier import BinaryClassifierLearner as Learner
  
  
  if __name__ == "__main__":
      if len(sys.argv) < 2:
          print("Usage: main.py DATA_FOLDER")
          exit(1)
  
      # Resolve data folder
      data_dir = os.path.abspath(sys.argv[1])
  
      # Fetch and preprocess dataset
      train_docs, test_docs, unused_docs = data.fetch_and_parse(data_dir)
      labels = []
      for d in train_docs + test_docs:
          for l in d.labels:
              if l not in labels:
                  labels.append(l)
  
      # Create and train model
      model = Learner(data_dir, labels, verbose=True)
      model.train(train_docs, test_docs=test_docs)
      model.save()
  
      # Predict labels for the test set
      predicted_docs = model.predict(nlp.clean(test_docs))
  
      for i in range(2):
          test_doc = test_docs[i]
          predicted_doc = predicted_docs[i]
  
          print("Text: " + str(test_doc.text[:200]))
          print("Expected Labels: " + str(test_doc.labels))
          print("Predicted Labels: " + str(predicted_doc.labels))
          print()
  
      print(evaluate(test_docs, predicted_docs, labels))
  
new_contents: """Main entry point for any experiments."""
  
  import os
  import sys
  import dataset.reuters21578.parser as data
  import nlp.utils as nlp
  from evaluation.mlc import evaluate
  from model.binary_classifier import BinaryClassifierLearner as Learner
  
  
  if __name__ == "__main__":
      if len(sys.argv) < 2:
          print("Usage: main.py DATA_FOLDER")
          exit(1)
  
      # Resolve data folder
      data_dir = os.path.abspath(sys.argv[1])
  
      # Fetch and preprocess dataset
      train_docs, test_docs, unused_docs = data.fetch_and_parse(data_dir)
      labels = []
      for d in train_docs + test_docs:
          for l in d.labels:
              if l not in labels:
                  labels.append(l)
  
      # Create and train model
      model = Learner(data_dir, labels, verbose=True)
      model.train(train_docs, test_docs=test_docs)
      model.save()
  
      # Predict labels for the test set
      predicted_docs = model.predict(nlp.clean(test_docs))
  
      # Print evaluation metrics
      print(evaluate(test_docs, predicted_docs, labels))
  
subject: remove excess code