def split(self):
    from utils.task_helpers import train_dev_test_split
    parser = ArgParseDefault(description='Split annotated data into training and test data set')
    parser.add_argument('--question', type=str, required=False, default='sentiment', help='Which data to load (has to be a valid question tag)')
    parser.add_argument('--name', type=str, required=False, default='', help='In case there are multiple cleaned labelled data output files give name of file (without csv ending), default: No name provided (works only if a single file is present).')
    parser.add_argument('--balanced-labels', dest='balanced_labels', action='store_true', default=False, help='Ensure equal label balance')
    parser.add_argument('--all-questions', dest='all_questions', action='store_true', default=False, help='Generate files for all available question tags. This overwrites the `question` argument. Default: False.')
    parser.add_argument('--label-tags', dest='label_tags', required=False, default=[], nargs='+', help='Only select examples with certain label tags')
    parser.add_argument('--has-label', dest='has_label', required=False, default='', help='Only select examples which have also been tagged with certain label')
    parser.add_argument('--dev-size', dest='dev_size', type=float, required=False, default=0.2, help='Fraction of dev size')
    parser.add_argument('--test-size', dest='test_size', type=float, required=False, default=0.2, help='Fraction of test size')
    parser.add_argument('--seed', type=int, required=False, default=42, help='Random state split')
    args = parser.parse_args(sys.argv[2:])
    train_dev_test_split(question=args.question, dev_size=args.dev_size, test_size=args.test_size, seed=args.seed, name=args.name, balanced_labels=args.balanced_labels, all_questions=args.all_questions, label_tags=args.label_tags, has_label=args.has_label)
def split(self):
    from utils.task_helpers import train_dev_test_split
    parser = ArgParseDefault(description='Split annotated data into training and test data set')
    parser.add_argument('--question', type=str, required=False, default='sentiment', help='Which data to load (has to be a valid question tag)')
    parser.add_argument('--name', type=str, required=False, default=, help='In case there are multiple cleaned labelled data output files give name of file (without csv ending), default: No name provided (works only if a single file is present).')
    parser.add_argument('--balanced-labels', dest='balanced_labels', action='store_true', default=False, help='Ensure equal label balance')
    parser.add_argument('--all-questions', dest='all_questions', action='store_true', default=False, help='Generate files for all available question tags. This overwrites the `question` argument. Default: False.')
    parser.add_argument('--label-tags', dest='label_tags', required=False, default=[], nargs='+', help='Only select examples with certain label tags')
    parser.add_argument('--has-label', dest='has_label', required=False, default=, help='Only select examples which have also been tagged with certain label')
    parser.add_argument('--dev-size', dest='dev_size', type=float, required=False, default=0.2, help='Fraction of dev size')
    parser.add_argument('--test-size', dest='test_size', type=float, required=False, default=0.2, help='Fraction of test size')
    parser.add_argument('--seed', type=int, required=False, default=42, help='Random state split')
    args = parser.parse_args(sys.argv[2:])
    train_dev_test_split(question=args.question, dev_size=args.dev_size, test_size=args.test_size, seed=args.seed, name=args.name, balanced_labels=args.balanced_labels, all_questions=args.all_questions, label_tags=args.label_tags, has_label=args.has_label)