What are transformers in deep learning?

Transformers are a type of neural network architecture designed to process sequential data, such as text, by capturing relationships between elements in a sequence. Introduced in the 2017 paper “Attention Is All You Need,” transformers rely heavily on self-attention mechanisms to weigh the importance of different parts of the input data. Unlike earlier models like RNNs or LSTMs, which process data step-by-step, transformers process entire sequences in parallel, making them faster to train and more effective at handling long-range dependencies. They have become the foundation for modern natural language processing (NLP) models like BERT and GPT.

The core of a transformer is its encoder-decoder structure, though some models use only one of these components. The encoder maps input data into a high-dimensional representation, while the decoder generates output based on that representation. The key innovation is multi-head self-attention, which allows the model to focus on different parts of the input simultaneously. For example, in a sentence like “The cat sat on the mat,” self-attention helps the model recognize that “cat” relates to “sat” and “mat.” Each “head” in multi-head attention learns distinct patterns, enabling richer context understanding. Additionally, transformers use positional encodings to inject information about the order of elements, since they lack built-in sequential processing.

Transformers excel in tasks like translation, text generation, and summarization. For instance, models like GPT-3 generate coherent paragraphs by predicting the next word in a sequence, while BERT improves tasks like question answering by analyzing bidirectional context. Beyond NLP, transformers have been adapted for computer vision (e.g., Vision Transformers for image classification) and even protein sequence analysis. Their parallel processing capability makes them efficient for large-scale training, but they require significant computational resources due to their complexity. For developers, frameworks like PyTorch and TensorFlow provide pre-built transformer layers, simplifying implementation while allowing customization for specific use cases.