An introduction to transformer models | Algolia

OpenAI and DeepMind both utilize transformer models, a type of neural network architecture designed to process sequential material such as sentences or time-series data. Transformer models have revolutionized the field with their ability to capture long-range dependencies and efficiently process sequential data. They're used widely in neural machine translation (NMT), natural language processing (NLP) tasks, computer vision, audio processing, and multi-modal processing. The transformer architecture works by understanding input data, determining the order of elements, passing the embedded and encoded input sequence through multiple encoder layers, and then feeding it through decoder layers to generate predictions for each position in the output sequence. Transformer models have significantly improved translation accuracy, language understanding, human-like text generation, sentiment analysis, content classification, voice assistants, image captioning, and ecommerce product search experiences.