What type of models are specifically designed to process sequential data using self-attention mechanisms?

Transformer models are specifically designed to process sequential data using self-attention mechanisms, making them particularly effective for tasks such as natural language processing and other applications where the context of the data points in a sequence matters. The self-attention mechanism allows the model to weigh the significance of different words or elements relative to each other in a sequence, enabling it to capture long-range dependencies more effectively than traditional recurrent models. This ability to focus on relevant parts of the input sequence, regardless of their positions, enhances the model's understanding and representation of the data.

In contrast, the other options do not utilize self-attention mechanisms in the same way. Neural networks can encompass a wide range of architectures, but they do not inherently include self-attention unless specifically designed as transformers. Support vector machines focus on finding hyperplanes to categorize data and do not process data in sequence, while random forests use decision tree ensembles for classification or regression tasks and are not designed for sequential data either. Thus, the unique architecture of transformer models, emphasizing self-attention, sets them apart as the correct answer for processing sequential data effectively.