code: import os
  import logging
  from pathlib import Path
  
  import numpy as np
  
  from sklearn.model_selection import train_test_split
  
  from finetune import Classifier
  from finetune.datasets import Dataset, generic_download
  
  logging.basicConfig(level=logging.DEBUG)
  
  SST_FILENAME = "SST-binary.csv"
  DATA_PATH = os.path.join('Data', 'Classify', SST_FILENAME)
  CHECKSUM = "02136b7176f44ff8bec6db2665fc769a"
  
  
  class StanfordSentimentTreebank(Dataset):
  
      def __init__(self, filename=None, **kwargs):
          super().__init__(filename=(filename or DATA_PATH), **kwargs)
  
      def md5(self):
          return CHECKSUM
          
      def download(self):
          """
          Download Stanford Sentiment Treebank to data directory
          """
          path = Path(self.filename)
          path.parent.mkdir(parents=True, exist_ok=True)
          generic_download(
              url="https://s3.amazonaws.com/enso-data/SST-binary.csv",
              text_column="Text",
              target_column="Target",
              filename=SST_FILENAME
          )
  
  
  if __name__ == "__main__":
      # Train and evaluate on SST
      dataset = StanfordSentimentTreebank(nrows=1000).dataframe
      model = Classifier(verbose=True, n_epochs=5, lr_warmup=0.1, tensorboard_folder='.tensorboard')
      trainX, testX, trainY, testY = train_test_split(dataset.Text.values, dataset.Target.values, test_size=0.3, random_state=42)
      model.fit(trainX, trainY)
      accuracy = np.mean(model.predict(testX) == testY)
      print('Test Accuracy: {:0.2f}'.format(accuracy))
  
apis: sklearn.model_selection.train_test_split