 ```python
from transformers import BertTokenizer, BertForSequenceClassification
from avalanche.training.strategies import Naive
from avalanche.data_loaders import MiniBatchSource, NumpyBatch
from avalanche.data_loaders.utils import pad_sequence_2D
import torch

class CustomDataCollator:
    def __init__(self, tokenizer):
        self.tokenizer = tokenizer

    def collate_fn(self, batch):
        input_ids = [example['input_ids'] for example in batch]
        attention_mask = [example['attention_mask'] for example in batch]
        decoder_input_ids = [example['decoder_input_ids'] for example in batch]
        decoder_attention_mask = [example['decoder_attention_mask'] for example in batch]
        labels = [example['labels'] for example in batch]

        input_ids = pad_sequence_2D(input_ids, padding_value=0)
        attention_mask = pad_sequence_2D(attention_mask, padding_value=0)
        decoder_input_ids = pad_sequence_2D(decoder_input_ids, padding_value=0)
        decoder_attention_mask = pad_sequence_2D(decoder_attention_mask, padding_value=0)
        labels = pad_sequence_2D(labels, padding_value=-100)

        return NumpyBatch({
            'input_ids': input_ids,
            'attention_mask': attention_mask,
            'decoder_input_ids': decoder_input_ids,
            'decoder_attention_mask': decoder_attention_mask,
            'labels': labels
        })

class CustomTranslationStrategy(Naive):
    def __init__(self, model, criterion, optimizer, data_collator, device):
        super().__init__(model, criterion, optimizer, data_collator, device)

    def forward(self, minibatch, return_loss=False):
        input_ids = minibatch['input_ids'].to(self.device)
        attention_mask = minibatch['attention_mask'].to(self.device)
        decoder_input_ids = minibatch['decoder_input_ids'].to(self.device)
        decoder_attention_mask = minibatch['decoder_attention_mask'].to(self.device)

        outputs = self.model(
            input_ids,
            attention_mask=attention_mask,
            decoder_input_ids=decoder_input_ids,
            decoder_attention_mask=decoder_attention_mask,
            labels=minibatch['labels']
        )

        if return_loss:
            return outputs.loss
        else:
            return outputs

def main():
    tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
    model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)
    criterion = torch.nn.CrossEntropyLoss()
    optimizer = torch.optim.Adam(model.parameters(), lr=1e-5)
    data_collator = CustomDataCollator(tokenizer)

    # Assume dataset is a Huggingface dataset
    dataset = ...

    preprocessed_dataset = dataset.map(lambda examples: {'input_ids': examples['input_ids'],
                                                          'attention_mask': examples['attention_mask'],
                                                          'decoder_input_ids': [tokenizer.convert_tokens_to_ids(tokenizer.convert_ids_to_tokens(ids))[1:] for ids in examples['input_ids']],
                                                          'decoder_attention_mask': [ids[1:] for ids in examples['attention_mask']],
                                                          'labels': examples['labels']},
                                        batched=True,
                                        batch_size=32,
                                        num_proc=4)

    strategy = CustomTranslationStrategy(model, criterion, optimizer, data_collator, 'cuda')

    # Assume experience is a tuple of (name, dataset)
    experiences = [('exp1', preprocessed_dataset),
                   ('exp2', preprocessed_dataset)]

    avalanche_dataset = MiniBatchSource(experiences, batch_size=32, shuffle=True, stratified=True)

    for experience in avalanche_dataset:
        strategy.train(experience.dataset, n_epochs=1)
        strategy.eval(experience.dataset)

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