Transformers in deep learning refer to a type of neural network architecture that has become widely popular for processing sequential data, particularly in natural language processing (NLP) tasks. Introduced in a 2017 paper titled "Attention is All You Need," transformers utilize a mechanism called self-attention to weigh the importance of different words in a sequence relative to one another. This allows the model to capture context more effectively than previous models, like recurrent neural networks (RNNs) or long short-term memory networks (LSTMs), which process data sequentially and can struggle with long-range dependencies.
One of the key features of transformers is their ability to process input data in parallel rather than sequentially. This parallelization not only speeds up training but also enables the model to learn relationships across the entire input sequence simultaneously. The self-attention mechanism computes a set of attention scores that determine how much focus to place on each word based on the context provided by the entire sequence. This contrasts with traditional sequential models, where the influence of earlier words diminishes over time, making it difficult for the model to retain context from longer sentences.
Transformers have been applied to various tasks, such as machine translation, text summarization, and text generation. For instance, models like BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer) are built on transformer architecture. They excel in tasks like sentiment analysis, where determining the context of words is crucial. Overall, transformers have transformed how we approach tasks that involve sequential data, leading to more effective and efficient models in a wide range of applications.