optim {
    seed = 0  # random seed
    learning_rate = 0.001
    batch_size = 16 # examples per batch
    max_iters = 4000  # max number of mini-batch steps to take
}

eval {
    num_examples = 10
    big_num_examples = 100
}

timing {
    eval_small = 100
    eval_big = 1000
}

model {
    word_dim = 300
    hidden_dim = 256  # hidden state dim of encoder and decoder
    agenda_dim = 256  # agenda vector dim
    vocab_size = 4998  # a proper size would be >20000
    attention_dim = 256
    encoder_layers = 2
    decoder_layers = 4
    wvec_path = github.txt
    num_copy_tokens = 200
    ident_pr = 0.1
    train_source_embeds = True
    train_target_embeds = True
    decoder_dropout_prob = 0.0
    encoder_dropout_prob = 0.0
    vae_kappa = 500
    full_vae = True
    use_vae = True
}

dataset {
    path = github
    source_cols = [0,1,2]
    target_col = 3
    seq_max = 150
}
