Transformer
The Transformer is a deep learning architecture introduced in the paper "Attention is All You Need" by Vaswani et al. in 2017. It represents a departure from previous sequence processing models like RNNs and LSTMs by relying entirely on self-attention mechanisms to weigh the significance of different parts of the input data.
The core idea is to model relationships between all parts of the input data, regardless of their positions, which allows for parallel processing and significantly reduces training times. Transformers consist of an encoder and decoder structure, where the encoder maps an input sequence to a continuous representation that the decoder then uses to generate an output sequence.
The multi-head attention mechanism allows the model to focus on different parts of the sequence for different tasks simultaneously. This architecture has proven to be highly effective, particularly in natural language processing (NLP) tasks such as translation, text summarization, and sentiment analysis, and has led to the development of models like BERT, GPT, and T5, which have set new standards for NLP applications.
A notable application of the Transformer architecture is in the development of the BERT (Bidirectional Encoder Representations from Transformers) model by Google. BERT has been used to achieve state-of-the-art performance in a wide range of NLP tasks, including question answering, language inference, and named entity recognition. BERT's key innovation is its bidirectional training of transformers, which allows it to understand the context of a word based on all of its surroundings (left and right of the word).
Another example is the GPT (Generative Pretrained Transformer) series by OpenAI, which demonstrates the Transformer's capabilities in generating coherent and contextually relevant text, opening new possibilities for AI-driven content creation, conversation agents, and more.
Additionally, the concept of Transformers has been extended to other domains like computer vision with the development of Vision Transformers (ViT), where the architecture is applied to sequences of image patches, showing that the Transformer's self-attention mechanism can effectively handle non-sequential data as well.