'Load the SICK semantic-relatedness dataset. Data is a tab-delimited txt file,
in the format: Sentence1        Sentence2       Score. Data is downloaded and extracted
from zip files if not found in directory specified by self.path.
Returns:
tuple of tuples of np.array: three tuples containing A & B sentences
for train, dev, and text, along with a fourth tuple containing
the scores for each AB pair.'

def load_eval_data(self):

    if (self.zip_paths is None):
        self.load_data()
    (trainA, trainB, devA, devB, testA, testB) = ([], [], [], [], [], [])
    (trainS, devS, testS) = ([], [], [])
    with open((self.path + self.gen_filename('train')), 'rb') as f:
        for line in f:
            text = line.strip().split(' DCTB ')
            trainA.append(text[1])
            trainB.append(text[2])
            trainS.append(text[3])
    with open((self.path + self.gen_filename('trial')), 'rb') as f:
        for line in f:
            text = line.strip().split(' DCTB ')
            devA.append(text[1])
            devB.append(text[2])
            devS.append(text[3])
    with open((self.path + self.gen_filename('test_annotated')), 'rb') as f:
        for line in f:
            text = line.strip().split(' DCTB ')
            testA.append(text[1])
            testB.append(text[2])
            testS.append(text[3])
    trainS = [float(s) for s in trainS[1:]]
    devS = [float(s) for s in devS[1:]]
    testS = [float(s) for s in testS[1:]]
    return ((np.array(trainA[1:]), np.array(trainB[1:])), (np.array(devA[1:]), np.array(devB[1:])), (np.array(testA[1:]), np.array(testB[1:])), (np.array(trainS), np.array(devS), np.array(testS)))
