Update README.md

README.md

@@ -108,7 +108,7 @@ if __name__ == "__main__":
                      'learning_rate': 5e-05, 'weight_decay': 0, 'num_train_epochs': 100, 'max_steps': -1, 'lr_scheduler_type': 'constant', 'do_eval': True, \
                      'evaluation_strategy': 'epoch', 'per_device_eval_batch_size': 32, 'logging_strategy': 'epoch', 'save_strategy': 'epoch', 'save_steps': 820, \
                      'dataloader_num_workers': 0, 'run_name': 'downstream_esm1b_localization_fix', 'optim': 'adamw_torch', 'resume_from_checkpoint': False, \
-                     'label_names': ['labels'], 'load_best_model_at_end': True, 'metric_for_best_model': 'accuracy', 'bf16': True}
+                     'label_names': ['labels'], 'load_best_model_at_end': True, 'metric_for_best_model': 'accuracy', 'bf16': True, "save_total_limit": 3}
     training_args = HfArgumentParser(TrainingArguments).parse_dict(training_args, allow_extra_keys=False)[0]
 
     def tokenize_protein(example, tokenizer=None):