Transformer Models

Transformer models are a class of deep learning architectures designed primarily for natural language processing (NLP) tasks. Introduced in the paper "Attention is All You Need" in 2017, these models leverage a mechanism called self-attention to process input data in parallel rather than sequentially, which allows them to efficiently handle long-range dependencies and contextual relationships within the data.Transformers consist of an encoder-decoder structure, where the encoder processes the input sequence and the decoder generates the output sequence. Each layer in the transformer includes multi-head self-attention mechanisms and feedforward neural networks. This architecture enables the model to weigh the importance of different words in a sequence relative to one another, thus capturing complex linguistic patterns and enhancing performance on various tasks such as translation, summarization, and text generation.Transformers have led to significant advancements in the field of NLP, giving rise to powerful pre-trained models like BERT and GPT, which can be fine-tuned for specific applications. Their ability to scale with data and compute resources has made them a foundational technology in modern AI research and application.