from transformers import Trainer, TrainingArguments, DataCollatorForLanguageModeling
from datasets import Dataset

def preprocess_data(df, tokenizer):
    df["text"] = df.apply(lambda row: f"Question: {row['Question']} Answer: {row['Answer']}", axis=1)
    dataset = Dataset.from_pandas(df)
    dataset = dataset.map(lambda x: tokenizer(x["text"], truncation=True, padding="max_length", max_length=512), batched=True)
    return dataset

def train_model(model, tokenizer, dataset, output_dir):
    training_args = TrainingArguments(
        output_dir=output_dir,
        per_device_train_batch_size=4,
        num_train_epochs=1,
        logging_dir="./logs",
        save_steps=10,
        logging_steps=10
    )
    data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=dataset,
        data_collator=data_collator
    )
    trainer.train()
    model.save_pretrained(output_dir)
    tokenizer.save_pretrained(output_dir)