Transformers
Transformers are a type of deep learning model that have been very successful in various natural language processing tasks, such as machine translation, text classification, and question answering. Transformers were introduced in 2017 by Vaswani et al. in the paper “Attention is All You Need”.
Transformers are a type of deep learning model that have been very successful in various natural language processing tasks, such as machine translation, text classification, and question answering. Transformers were introduced in 2017 by Vaswani et al. in the paper “Attention is All You Need”.
The key idea behind transformers is the use of self-attention mechanisms, which allow the model to weight the importance of different parts of the input data when making predictions. This allows transformers to effectively capture long-range dependencies in the data, and to make use of context information in a flexible and scalable way.
Transformers consist of a series of self-attention layers, interleaved with feed-forward layers, and are trained end-to-end on large amounts of data. They are designed to be highly parallelizable, allowing for efficient training on GPUs and other parallel hardware.
Transformers have been used to achieve state-of-the-art results on a wide range of NLP tasks, and they have become a popular choice for building NLP systems. They have also been adapted to other domains, such as computer vision, and are being used in a growing number of applications, including language generation, sentiment analysis, and summarization.
Overall, transformers have been a major advancement in deep learning and have helped to drive innovation and progress in the field of NLP. They are expected to continue to play a significant role in the development of NLP and other artificial intelligence systems in the future.
