diff --git a/allegro.py b/allegro.py
index a7ff189..53b9036 100644
--- a/allegro.py
+++ b/allegro.py
@@ -1,7 +1,5 @@
-from transformers import MarianForCausalLM, MarianTokenizer, Trainer, TrainingArguments
-from datasets import load_dataset
+from transformers import MarianForCausalLM, MarianTokenizer, TrainingArguments
 
-# Załaduj model i tokenizer
 model_name = "allegro/multislav-5lang"
 model = MarianForCausalLM.from_pretrained(model_name)
 tokenizer = MarianTokenizer.from_pretrained(model_name)
@@ -9,39 +7,4 @@ tokenizer = MarianTokenizer.from_pretrained(model_name)
 model.save_pretrained("./models/ably")
 tokenizer.save_pretrained("./models/ably")
 
-print("✅ Model został wytrenowany i zapisany!")
-
-# Załaduj dane (przykład dla tłumaczenia z języka rumuńskiego na angielski)
-#dataset = load_dataset("wmt16", "ro-en")
-
-#def tokenize_function(examples):
-#    # Tokenizacja
-#    tokenized = tokenizer([example['en'] for example in examples['translation']], 
-#                          [example['ro'] for example in examples['translation']], 
-#                          truncation=True, padding='max_length', max_length=128)
-#    # Ustawienie labels
-#    tokenized['labels'] = tokenized['input_ids'].copy()
-#    return tokenized
-
-#tokenized_datasets = dataset.map(tokenize_function, batched=True)
-
-# Skonfiguruj trenera
-training_args = TrainingArguments(
-    output_dir="./results",
-    evaluation_strategy="epoch",
-    learning_rate=5e-5,
-    per_device_train_batch_size=4,
-    per_device_eval_batch_size=4,
-    num_train_epochs=3,
-    weight_decay=0.01,
-)
-
-#trainer = Trainer(
-#    model=model,
-#    args=training_args,
-#    train_dataset=tokenized_datasets["train"],
-#    eval_dataset=tokenized_datasets["test"],
-#)
-
-# Trening modelu
-#trainer.train()
\ No newline at end of file
+print("✅ Model został wytrenowany i zapisany!")
\ No newline at end of file