def parse_args()

in notebooks/text-classification/scripts/train.py [0:0]

• 55 lines of code
• 2 McCabe index (conditional complexity)

def parse_args():
    """Parse the arguments."""
    parser = argparse.ArgumentParser()
    # add model id and dataset path argument
    parser.add_argument(
        "--model_id",
        type=str,
        default="bert-base-uncased",
        help="Model id to use for training.",
    )
    parser.add_argument(
        "--output_dir",
        type=str,
        default=None,
        help="Hugging Face Repository id for uploading models",
    )
    parser.add_argument(
        "--repository_id",
        type=str,
        default=None,
        help="Hugging Face Repository id for uploading models",
    )
    parser.add_argument("--epochs", type=int, default=3, help="Number of epochs to train for.")
    parser.add_argument("--max_steps", type=int, default=-1, help="Number of steps to train for.")
    parser.add_argument(
        "--per_device_train_batch_size",
        type=int,
        default=8,
        help="Batch size to use for training.",
    )
    parser.add_argument(
        "--per_device_eval_batch_size",
        type=int,
        default=8,
        help="Batch size to use for validation.",
    )
    parser.add_argument(
        "--train_max_length",
        type=int,
        default=128,
        help="Maximum length of tokens to be used for training.",
    )
    parser.add_argument(
        "--learning_rate",
        type=float,
        default=5e-5,
        help="Learning rate to use for training.",
    )
    parser.add_argument("--seed", type=int, default=42, help="Seed to use for training.")
    parser.add_argument(
        "--hf_token",
        type=str,
        default=HfFolder.get_token(),
        help="Token to use for uploading models to Hugging Face Hub.",
    )
    args = parser.parse_known_args()
    return args