Transformers use self-attention mechanisms to weigh the importance of different words in a sentence, while RNNs use hidden states to capture temporal dependencies between inputs. Therefore, the choice between using a Transformer or an RNN depends on the nature of the input data and the task at hand.
RNNs (Recurrent Neural Networks) are designed for sequential data, while Transformers are specifically built for non-sequential textual inputs. This means that a transformer is better suited at processing unstructured or semi-structured input such as natural language texts and images, whereas RNNs excel at structuring the sequence of events, like speech recognition tasks where they can model dependencies between words, phrases, etc.
spam fails_task lang_mismatch pii not_appropriate hate_speech sexual_content quality toxicity humor helpfulness creativity violence