While recurring neural network (RNN) models are powerful for tasks that demand fast inference and domain-specific tuning, such as e-commerce, Transformer RNN models excel at handling complex language understanding and are well-suited for common AI tasks like classification, prediction, and sequence modeling. Because a transformer RNN model uses a frozen pre-trained transformer (FPT) base, its customizability is limited. And due to the complexity of the transformer architecture, it requires more training and inference time than RNN-only models. However, it also delivers high-quality results with minimal tuning, even when your data is limited or noisy. To help you get started faster, Lucidworks AI provides the models below as pre-trained bases: