def sst_binary()

in utils.py [0:0]

• 5 lines of code
• 1 McCabe index (conditional complexity)

def sst_binary(data_dir='data/'):
    """
    Most standard models make use of a preprocessed/tokenized/lowercased version
    of Stanford Sentiment Treebank. Our model extracts features from a version
    of the dataset using the raw text instead which we've included in the data
    folder.
    """
    trX, trY = load_sst(os.path.join(data_dir, 'train_binary_sent.csv'))
    vaX, vaY = load_sst(os.path.join(data_dir, 'dev_binary_sent.csv'))
    teX, teY = load_sst(os.path.join(data_dir, 'test_binary_sent.csv'))
    return trX, vaX, teX, trY, vaY, teY