def main()

in train_simple.py [0:0]

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

def main(
    batch_size: int = 32,
    max_ctx: int = 1024,
    ds_name: str = "sciq",
    loss: str = "xent",
    n_docs: int = 20000,
    n_test_docs: int = 10000,
    model_size: str = "gpt2",
    lr: Optional[float] = None,
    optim: Optional[str] = None,
    epochs: int = 2,
    force_retrain: bool = False,
    seed: int = 0,
    minibatch_size_per_device: Optional[float] = None,
    train_with_dropout: bool = False,
    results_folder: str = "/tmp/results",
    linear_probe: bool = False,
    lr_schedule: str = "cosine_anneal",
    # Note: you can pass either weak_model_size or weak_labels_path. If you pass
    # weak_model_size, we will guess the path to the weak labels based on the weak
    # model. If you pass weak_labels_path, we will use that path instead.
    # If you pass neither, we will train on ground truth.
    weak_model_size: Optional[str] = None,
    weak_labels_path: Optional[str] = None,
    sweep_subfolder: str = "default",
    # Set to a very large value so that by default we don't do any intermediate evals but
    # still do final evals (which requires eval_every to be set to a non-zero, non-None value)
    eval_every: int = 1000000,
    sync_command: Optional[str] = None,