Transformaattorimalleja

Transformaattorimalleja, often referred to simply as transformers, are a class of deep learning models that have revolutionized natural language processing and are increasingly applied to other domains. Introduced in the paper "Attention Is All You Need" by Vaswani et al. in 2017, transformers are built upon a mechanism called self-attention. This mechanism allows the model to weigh the importance of different words in an input sequence when processing each word. Unlike previous recurrent neural network (RNN) or convolutional neural network (CNN) based models, transformers can process sequences in parallel, leading to significant speedups in training and inference.

The core architecture of a transformer consists of an encoder and a decoder. The encoder processes the input sequence and creates a representation, while the decoder uses this representation to generate an output sequence. Both the encoder and decoder are composed of multiple identical layers, each containing a multi-head self-attention mechanism and a feed-forward neural network. The multi-head attention allows the model to jointly attend to information from different representation subspaces at different positions. Positional encodings are added to the input embeddings to inject information about the relative or absolute position of tokens in the sequence, as the self-attention mechanism itself does not inherently consider order.

Transformer models have demonstrated state-of-the-art performance on a wide range of tasks, including machine translation, text summarization, question answering, and text generation. Notable examples include BERT (Bidirectional Encoder Representations from Transformers), GPT (Generative Pre-trained Transformer) series, and T5 (Text-to-Text Transfer Transformer). Their ability to capture long-range dependencies in data and their scalability have made them a cornerstone of modern artificial intelligence research and development.