Transformerpõhiseid

Transformerpõhiseid refers to something based on the transformer architecture. The transformer is a deep learning model architecture introduced in the 2017 paper "Attention Is All You Need". It has become a foundational element in many state-of-the-art natural language processing (NLP) models. The core innovation of the transformer is its reliance on self-attention mechanisms, which allow the model to weigh the importance of different words in an input sequence when processing it. This contrasts with previous dominant architectures like recurrent neural networks (RNNs) and convolutional neural networks (CNNs) that processed data sequentially or with fixed local receptive fields.

Transformerpõhiseid models excel at tasks requiring an understanding of long-range dependencies within data, such as machine translation, text summarization, and question answering. Their ability to process sequences in parallel, rather than strictly sequentially, also makes them more efficient to train on large datasets. Popular examples of transformerpõhiseid models include BERT, GPT (Generative Pre-trained Transformer), and T5. These models are pre-trained on massive amounts of text data and can then be fine-tuned for specific downstream tasks. The transformer architecture's success has extended beyond NLP, with adaptations being explored in computer vision and other domains. The fundamental principle of attention allows these models to dynamically focus on relevant parts of the input, leading to significant improvements in performance across a wide range of AI applications.