This chapter presents an overview of the state of the art in natural language processing, exploring one specific computational architecture, the Transformer model, which plays a central role in a wide range of applications. This architecture condenses many advances in neural learning methods and can be exploited in many ways : to learn representations for linguistic entities ; to generate coherent utterances and answer questions ; to perform utterance transformations, an illustration being their automatic translation. These different facets of the architecture will be successively presented, also allowing us to discuss its limitations.