init

training_scripts/finetune_t5.py

@@ -136,9 +136,9 @@ def train(
         return get_f1_score(references_decode, generation_decode)
 
     if not skip_train:
-        lr = [1e-6, 1e-4] if lr is None else lr
+        lr = [1e-6, 1e-5, 1e-4] if lr is None else lr
         batch = [64] if not batch else batch
-        epoch = [1, 3, 5] if not epoch else epoch
+        epoch = [3, 5] if not epoch else epoch
         eval_batch_size = min(batch) if not eval_batch_size else eval_batch_size
         for n, (lr_tmp, batch_tmp, epoch_tmp) in enumerate(product(lr, batch, epoch)):
             logging.info(f"[TRAIN {n}/{len(lr) * len(batch) * len(epoch)}] lr: {lr_tmp}, batch: {batch_tmp}")
@@ -185,6 +185,7 @@ def train(
         for eval_file in glob(f"{output_dir}/model_*/eval_results.json"):
             with open(eval_file) as f:
                 model_score[os.path.dirname(eval_file)] = json.load(f)['eval_f1']
+        logging.info(f"- Search Result\n{json.dumps(model_score, indent=4)}")
         best_model = max(model_score, key=model_score.get)
         copy_tree(best_model, f'{output_dir}/best_model')
     else: