Instead of focusing solely on sequence length, I propose optimizing the data loading and preprocessing within the `ToxicityDataset` class. Specifically, I suggest pre-tokenizing the entire dataset and storing the tokenized inputs and attention masks in memory during initialization. This eliminates redundant tokenization during training, significantly speeding up data loading. Furthermore, I will explore using `np.array` instead of `torch.tensor` for labels initially, converting to tensors only when needed in `__getitem__`, potentially reducing overhead. This approach prioritizes efficient data handling to improve training speed and potentially model performance.
