transformerpõhised

Transformerpõhised refers to a category of machine learning models that are based on the Transformer architecture. This architecture was first introduced in the paper "Attention Is All You Need" in 2017 and has since become a dominant force in natural language processing and is increasingly being applied to other domains like computer vision.

The core innovation of the Transformer architecture is its reliance on self-attention mechanisms. Unlike previous recurrent neural network (RNN) based models that processed sequences sequentially, Transformers process entire sequences in parallel. This parallel processing, combined with the ability of self-attention to weigh the importance of different parts of the input sequence, allows Transformers to capture long-range dependencies more effectively and efficiently.

Transformerpõhised models have demonstrated state-of-the-art performance across a wide range of tasks, including machine translation, text summarization, question answering, and text generation. Notable examples of Transformerpõhised models include BERT (Bidirectional Encoder Representations from Transformers), GPT (Generative Pre-trained Transformer) series, and T5 (Text-to-Text Transfer Transformer). These models are often pre-trained on massive datasets and then fine-tuned for specific downstream tasks. The success of Transformerpõhised models has led to significant advancements in artificial intelligence research and applications.