The Transformer architecture is a deep learning model designed to process sequential data, such as text, by relying entirely on attention mechanisms rather than recurrence or convolution. Introduced in the seminal paper Attention is All You Need (2017), it has become the foundation for modern NLP models like BERT and GPT.
At its core, the Transformer uses self-attention mechanisms to calculate the importance of each word relative to others in a sequence. This allows the model to capture long-range dependencies and context effectively. It consists of an encoder-decoder structure, where the encoder processes the input sequence and the decoder generates the output sequence. Each layer in the architecture includes multi-head attention and feed-forward networks, enabling the model to focus on multiple aspects of context simultaneously.
Transformers are highly parallelizable, making them computationally efficient for training on large datasets. Their ability to capture complex relationships has led to breakthroughs in machine translation, text summarization, question answering, and other NLP tasks. Tools like Hugging Face Transformers provide pre-trained transformer models that can be fine-tuned for specific applications, making this architecture accessible to developers.