init

training_scripts/finetune_t5.py

@@ -161,12 +161,12 @@ def train(
                 compute_metrics=compute_metric,
             )
             os.makedirs(f'{output_dir}/model_{n}', exist_ok=True)
-            best_run = trainer.train()
+            trainer.train()
             trainer.save_model(f'{output_dir}/model_{n}')
             tokenizer.save_pretrained(f'{output_dir}/model_{n}')
             # grid search
             with open(f'{output_dir}/model_{n}/hyperparameters.json', 'w') as f:
-                json.dump(best_run.hyperparameters, f)
+                json.dump({"learning_rate": lr_tmp, "batch_size": batch_tmp}, f)
             del trainer
             gc.collect()
             torch.cuda.empty_cache()