Update train.py

train.py

@@ -6,7 +6,7 @@ from transformers import T5ForConditionalGeneration, T5Tokenizer, Trainer, Train
 os.environ["HF_HOME"] = "/app/hf_cache"
 os.environ["HF_DATASETS_CACHE"] = "/app/hf_cache"
 os.environ["TRANSFORMERS_CACHE"] = "/app/hf_cache"
-
+os.makedirs("/app/t5-finetuned", exist_ok=True)
 # Load dataset
 dataset = load_dataset("tatsu-lab/alpaca")  # Change if using your dataset
 
@@ -45,7 +45,7 @@ print("Dataset successfully split and tokenized.")
 
 #  Define training arguments
 training_args = TrainingArguments(
-    output_dir="./t5-finetuned",
+    output_dir="/app/t5-finetuned",
     per_device_train_batch_size=2,  # Lowered to avoid memory issues
     per_device_eval_batch_size=2,
     num_train_epochs=1,  # Test run (increase for full fine-tuning)