init

training_scripts/finetune_t5.py

@@ -181,12 +181,19 @@ def train(
             torch.cuda.empty_cache()
             # cuda.get_current_device().reset()
 
-        model_score = {}
+        model_score = []
         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)
+                results = json.load(f)
+                model_score.append([os.path.dirname(eval_file), results['eval_loss'], results['eval_f1']])
+        logging.info("Search Result")
+        for i in model_score:
+            logging.info(i)
+        max_metric = max(model_score, key=lambda x: x[2])
+        if len([i for i in model_score if i[2] == max_metric]) > 1:
+            best_model = sorted(model_score, key=lambda x: x[1])[0][0]
+        else:
+            best_model = sorted(model_score, key=lambda x: x[2])[-1][0]
         copy_tree(best_model, f'{output_dir}/best_model')
     else:
         logging.info('skip hyperparameter search & model training (already done)')

training_scripts/script.sh

@@ -1,4 +1,4 @@
-python finetune_t5.py --dataset-name ja --low-cpu-mem-usage --model-alias mt5-small-tweet-topic-ja --model-organization cardiffnlp
+python finetune_t5.py --dataset-name ja --low-cpu-mem-usage --model-alias mt5-small-tweet-topic-ja --model-organization cardiffnlp --skip-upload
 python finetune_t5.py --dataset-name gr --low-cpu-mem-usage --model-alias mt5-small-tweet-topic-gr --model-organization cardiffnlp
 python finetune_t5.py --dataset-name es --low-cpu-mem-usage --model-alias mt5-small-tweet-topic-es --model-organization cardiffnlp
 python finetune_t5.py --dataset-name en --low-cpu-mem-usage --model-alias mt5-small-tweet-topic-en --model-organization cardiffnlp