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README.md

@@ -69,7 +69,7 @@ Finally, type `sh train.sh` in the console. Wating for the training ends~
 ## Testing (to reproduce, optional)
 Similar to fine-tuning, prepare `test.sh` as below:
 ```
-python inference.py \
+python official_test.py \
  --data_path ScImpactPredict/NAID/NAID_test_extrainfo.csv \
  --weight_dir path_to_runs_dir
 ```

offcial_train.py

@@ -101,7 +101,7 @@ def main(args):
     train_size = int(0.9 * total_size)
     val_size = total_size - train_size
     train_dataset, val_dataset = random_split(total_dataset, [train_size, val_size])
-    test_dataset = TextDataset(df_test, tokenizer, args.max_length)
+    test_dataset = TextDataset(df_test, tokenizer, args.max_length) # DO NOT USE FOR PARAMETER SEARCHING
 
     # Prepare Accelerator
     accelerator = Accelerator()
@@ -171,7 +171,6 @@ def get_args():
                         help='Directory for storing TensorBoard logs and model checkpoints')
 
     # Dataset and training configuration
-    parser.add_argument('--batch_size', type=int, default=16, help='Batch size for training and validation')
     parser.add_argument('--total_epochs', type=int, default=5, help='Total number of epochs to train')
     parser.add_argument('--base_lr', type=float, default=5e-5, help='Base learning rate for the optimizer')
     parser.add_argument('--learning_rate', type=float, default=1e-4, help='Learning rate for the optimizer')