(self)
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)))
Load the SICK semantic-relatedness dataset. Data is a tab-delimited txt file,
        in the format: Sentence1\tSentence2\tScore. 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.
Load the SICK semantic-relatedness dataset. Data is a tab-delimited txt file,
        in the format: Sentence1\tSentence2\tScore. Data is downloaded and extracted
        from zip files if not found in directory specified by self.path.
Load the SICK semantic - relatedness dataset . Data is a tab - delimited txt file in the format : Sentence1 \ tSentence2 \ tScore . Data is downloaded and extracted from zip files if not found in directory specified by self . path .
def load_eval_data(self):
        """
        Load the SICK semantic-relatedness dataset. Data is a tab-delimited txt file,
        in the format: Sentence1\tSentence2\tScore. 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.
        """
        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(b'\t')
                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(b'\t')
                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(b'\t')
                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)))
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 ( b'\t' ) 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 ( b'\t' ) 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 ( b'\t' ) 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 ) ) )