What are "transformers" in AI technology?

Transformers are a groundbreaking model architecture in the field of artificial intelligence, particularly for natural language processing tasks. This architecture incorporates self-attention mechanisms that allow the model to weigh the importance of different words in a sentence relative to one another, regardless of their position in the text.

This ability enables transformers to capture long-range dependencies and contextual relationships more effectively than previous architectures, such as recurrent neural networks (RNNs). Through self-attention, transformers can process input sequences in parallel, leading to faster training times and improved performance on a variety of tasks, including translation, summarization, and text generation.

The other options reference different aspects of technology and AI but do not capture the specific role and function of transformers in deep learning. For instance, while hardware components are vital for processing, they do not define the architecture itself. Similarly, programming languages and methods for optimizing training datasets are not inherently related to the unique capabilities that transformers bring to AI modeling.