What are Transformers in Machine-Learning

A transformer is a type of neural network architecture used in natural language processing (NLP) that was introduced in the paper "Attention is All You Need" by Vaswani et al. in 2017. It is designed to handle sequential data, such as text, by incorporating a mechanism called self-attention.

In a transformer network, the input sequence is first embedded into a vector space, and then processed through multiple layers of self-attention and feed-forward neural networks. The self-attention mechanism allows the network to focus on different parts of the input sequence at each layer, based on their relevance to the task being performed. This allows the network to capture long-range dependencies and relationships between different parts of the input sequence.

One of the key advantages of transformer networks is their ability to handle variable-length inputs, which is important for many NLP tasks such as machine translation and text summarization. In addition, transformers have achieved state-of-the-art performance on many NLP benchmarks, surpassing previous models that relied on recurrent neural networks (RNNs) or convolutional neural networks (CNNs).

Overall, transformers represent an important advancement in the field of NLP and have led to many new applications and improvements in language modeling, text classification, and other related tasks.


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