```python
import torch
from transformers import T5ForConditionalGeneration, T5Tokenizer
from avalanche.benchmarks.classic import SplitMNIST
from avalanche.models import SimpleMLP
from avalanche.training.strategies import Naive
from avalanche.evaluation.metrics import accuracy_metrics, loss_metrics
from avalanche.logging import InteractiveLogger
from avalanche.training.plugins import EvaluationPlugin
from datasets import load_dataset
from torch.utils.data import DataLoader, Dataset
from torch.optim import AdamW

class CustomNaive(Naive):
    def __init__(self, model, optimizer, criterion=torch.nn.CrossEntropyLoss(), train_mb_size=10, train_epochs=1, eval_mb_size=10, device=None, plugins=None, evaluator=None, eval_every=-1):
        super().__init__(model, optimizer, criterion, train_mb_size, train_epochs, eval_mb_size, device, plugins, evaluator, eval_every)

def preprocess_data(dataset):
    tokenizer = T5Tokenizer.from_pretrained('t5-small')
    inputs = []
    targets = []
    for item in dataset:
        question = item['question']
        context = item['context']
        answer = item['answers']['text'][0]
        input_text = f"question: {question} context: {context} </s>"
        target_text = f"{answer} </s>"
        input_encodings = tokenizer(input_text, padding='max_length', truncation=True, max_length=512)
        target_encodings = tokenizer(target_text, padding='max_length', truncation=True, max_length=128)
        inputs.append(input_encodings)
        targets.append(target_encodings)
    return inputs, targets

class SQuADDataset(Dataset):
    def __init__(self, encodings, labels):
        self.encodings = encodings
        self.labels = labels

    def __getitem__(self, idx):
        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
        item['labels'] = torch.tensor(self.labels['input_ids'][idx])
        return item

    def __len__(self):
        return len(self.labels['input_ids'])

def main():
    dataset = load_dataset("squad")
    train_dataset = dataset['train']
    val_dataset = dataset['validation']

    train_inputs, train_targets = preprocess_data(train_dataset)
    val_inputs, val_targets = preprocess_data(val_dataset)

    train_dataset = SQuADDataset(train_inputs, train_targets)
    val_dataset = SQuADDataset(val_inputs, val_targets)

    model = T5ForConditionalGeneration.from_pretrained('t5-small')
    tokenizer = T5Tokenizer.from_pretrained('t5-small')
    optimizer = AdamW(model.parameters(), lr=5e-5)

    strategy = CustomNaive(model=model, optimizer=optimizer, train_mb_size=16, train_epochs=3, eval_mb_size=64, device='cuda')

    # Assuming the training loop is handled by the CustomNaive strategy
    strategy.train(train_dataset)
    strategy.eval(val_dataset)

    # Example of asking a question
    question = "What is the capital of France?"
    context = "France is a country in Europe. Paris is the capital of France."
    input_text = f"question: {question} context: {context} </s>"
    input_ids = tokenizer(input_text, return_tensors="pt").input_ids
    outputs = model.generate(input_ids)
    answer = tokenizer.decode(outputs[0])

    print(f"Question: {question}")
    print(f"Answer: {answer}")

if __name__ == "__main__":
    main()
```