INNER CODE UNIT · Python
base_setting
yixinL7/BRIO · main.py:28
def base_setting(args):
args.batch_size = getattr(args, 'batch_size', 1) # batch size on one gpu, one step
args.epoch = getattr(args, 'epoch', 100)
args.report_freq = getattr(args, "report_freq", 100) # report frequency
args.accumulate_step = getattr(args, "accumulate_step", 32) # accumulate gradients steps
args.margin = getattr(args, "margin", 0.001) # margin for ranking loss on candidate summaries
args.gold_margin = getattr(args, "gold_margin", 0) # margin for ranking loss on gold summaries
args.gold_weight = getattr(args, "gold_weight", 0) # weight for ranking loss on gold summaries
args.mle_weight = getattr(args, "mle_weight", 1) # weight for mle loss on gold summaries
args.rank_weight = getattr(args, "rank_weight", 1) # weight for ranking loss on candidate summaries
args.model_type = getattr(args, "model_type", "facebook/bart-large-cnn") # model type
args.warmup_steps = getattr(args, "warmup_steps", 10000) # warmup steps
args.normalize = getattr(args, "normalize", True) # normalize predicited likelihood
args.grad_norm = getattr(args, "grad_norm", 0) # gradient norm
args.seed = getattr(args, "seed", 970903) # random seed
args.no_gold = getattr(args, "no_gold", False) # whether to use gold summaries
args.pretrained = getattr(args, "pretrained", None) # pretrained model path
args.max_lr = getattr(args, "max_lr", 2e-3) # max learning rate (* 1e-2)