transzformeralapú

Transzformeralapú refers to artificial intelligence models that utilize the transformer architecture. This architecture, first introduced in the 2017 paper "Attention Is All You Need," revolutionized natural language processing and has since been applied to a wide range of other domains, including computer vision and audio processing.

The core innovation of the transformer architecture is its reliance on self-attention mechanisms. Unlike previous recurrent neural networks (RNNs) or convolutional neural networks (CNNs) that process data sequentially or locally, self-attention allows the model to weigh the importance of different parts of the input data regardless of their position. This enables transformers to capture long-range dependencies and contextual relationships more effectively, leading to significant improvements in tasks such as machine translation, text summarization, and question answering.

The encoder-decoder structure is a common component of transformer models. The encoder processes the input sequence, creating a representation, and the decoder uses this representation to generate an output sequence. Both components heavily utilize self-attention and feed-forward neural networks. The scalability and parallelization capabilities of transformers have also made them highly efficient for training on large datasets, contributing to their widespread adoption in modern AI research and development. Examples of prominent transzformeralapú models include BERT, GPT, and T5.