Transformers in NLP.
Transformers are a type of deep learning architecture used in natural language processing (NLP) tasks, such as language translation, sentiment analysis, and question-answering. They were first introduced in the paper "Attention Is All You Need" by Vaswani et al. in 2017, and have since become the dominant architecture in the field of NLP. The key innovation of transformers is the use of self-attention mechanisms, which allow the model to selectively focus on different parts of the input sequence when making predictions. This is in contrast to earlier NLP models, which typically used recurrent neural networks (RNNs) or convolutional neural networks (CNNs) to process sequences. In a transformer model, the input sequence is first embedded into a high-dimensional vector space, where each element of the sequence is represented by a vector. These embedded vectors are then fed into multiple layers of self-attention and feedforward neural networks, which progressively refin