def METHOD_NAME(args):
    # load dataset
    dataset = FlairDataset(args.input, args.out_folder)
    train, test, val = dataset.train_test_val_split(args.ratios)
    # train Flair
    trainer = FlairTrainer()
    trainer.create_flair_corpus(train, test, val, to_bio=True)
    # read privy dataset
    privy_corpus = trainer.read_corpus("./")
    # Transformer
    if args.transformer or not (args.rnn or args.rnn_fast):
        trainer.train_with_transformers(privy_corpus, mini_batch_size=32,
                                        embeddings="distilbert-base-cased", max_epochs=20)
    # GloVe embeddings
    if args.rnn:
        trainer.train_with_flair_embeddings(privy_corpus)
    if args.rnn_fast:
        trainer.train_with_flair_embeddings(privy_corpus, fast=True)