In the context of artificial intelligence (AI), a Transformer is a type of model architecture used in the field of deep learning, specifically in natural language processing (NLP). The Transformer model was introduced in a paper titled „Attention is All You Need“ by Vaswani et al., from Google Brain, in 2017.
Transformers represent a departure from the traditional sequence-to-sequence model architectures used for tasks like machine translation and text summarization. Instead of relying on recurrent neural networks (RNNs) or convolutional neural networks (CNNs), Transformers use a mechanism called „attention“ to weigh the influence of different input words on each output word. In other words, instead of processing words in a sentence in a sequential order, the Transformer can process all words in the sentence at once, making it much more parallelizable.
The primary advantage of the Transformer architecture is its ability to handle long-range dependencies in text more effectively. Traditional RNN-based models often struggle with remembering earlier inputs in a sequence, which can be a problem in language tasks where understanding the context is crucial.
Transformer models have been the basis for several significant subsequent models in NLP, including OpenAI’s GPT (Generative Pretrained Transformer) and Google’s BERT (Bidirectional Encoder Representations from Transformers), both of which have achieved state-of-the-art performance on a variety of language tasks.