def get_model_args()

in codes/rnn_models.py [0:0]

• 59 lines of code
• 1 McCabe index (conditional complexity)

def get_model_args():
    parser = argparse.ArgumentParser()
    # /////////NLI-Args//////////////
    parser = argparse.ArgumentParser(description="NLI training")
    # paths
    parser.add_argument(
        "--nlipath", type=str, default="ocnli", help="NLI data (mnli or snli)"
    )
    parser.add_argument(
        "--outputdir", type=str, default="rnn_models/", help="Output directory"
    )
    parser.add_argument("--outputmodelname", type=str, default="model.pickle")
    # training
    parser.add_argument("--n_epochs", type=int, default=20)
    parser.add_argument("--batch_size", type=int, default=64)
    parser.add_argument(
        "--dpout_model", type=float, default=0.0, help="encoder dropout"
    )
    parser.add_argument(
        "--dpout_fc", type=float, default=0.0, help="classifier dropout"
    )
    parser.add_argument(
        "--nonlinear_fc", type=float, default=0, help="use nonlinearity in fc"
    )
    parser.add_argument(
        "--optimizer", type=str, default="sgd,lr=0.1", help="adam or sgd,lr=0.1"
    )
    parser.add_argument(
        "--lrshrink", type=float, default=5, help="shrink factor for sgd"
    )
    parser.add_argument("--decay", type=float, default=0.99, help="lr decay")
    parser.add_argument("--minlr", type=float, default=1e-5, help="minimum lr")
    parser.add_argument(
        "--max_norm", type=float, default=5.0, help="max norm (grad clipping)"
    )
    # model
    parser.add_argument(
        "--encoder_type", type=str, default="InferSent", help="see list of encoders"
    )
    parser.add_argument(
        "--enc_lstm_dim", type=int, default=200, help="encoder nhid dimension"
    )  # 2048
    parser.add_argument(
        "--n_enc_layers", type=int, default=1, help="encoder num layers"
    )
    parser.add_argument("--fc_dim", type=int, default=200, help="nhid of fc layers")
    parser.add_argument(
        "--n_classes", type=int, default=5, help="entailment/neutral/contradiction"
    )
    parser.add_argument("--pool_type", type=str, default="max", help="max or mean")

    # gpu
    parser.add_argument("--gpu_id", type=int, default=3, help="GPU ID")
    parser.add_argument("--seed", type=int, default=100, help="seed")

    # data
    parser.add_argument(
        "--word_emb_dim", type=int, default=300, help="word embedding dimension"
    )
    parser.add_argument(
        "--word_emb_type",
        type=str,
        default="normal",
        help="word embedding type, either glove or normal",
    )

    params, _ = parser.parse_known_args()
    return params