deve

app.py

@@ -220,8 +220,8 @@ def fine_tune_model(model, dataset_name, hub_id, api_key, num_epochs, batch_size
         dataset = load_dataset(dataset_name.strip())
                 
         #dataset['train'] = dataset['train'].select(range(8000))
-        dataset['train'] = dataset['train'].select(range(1000))
-        dataset['validation'] = dataset['validation'].select(range(100))
+        dataset['train'] = dataset['train'].select(range(4000))
+        dataset['validation'] = dataset['validation'].select(range(200))
         train_set = dataset.map(tokenize_function, batched=True)
         #valid_set = dataset['validation'].map(tokenize_function, batched=True)
         
@@ -242,7 +242,7 @@ def fine_tune_model(model, dataset_name, hub_id, api_key, num_epochs, batch_size
         )    
     
         # Fine-tune the model
-        trainer.evaluate()
+        trainer.train()
         #if os.path.exists(training_args.output_dir) and os.listdir(training_args.output_dir):
             #train_result = trainer.train(resume_from_checkpoint=True)
         #else: