A transformer is a deep learning model that adopts the mechanism of self-attention, differentially weighting the significance of each part of the input data. It is used primarily in the fields of natural language processing (NLP) and computer vision (CV).

Like recurrent neural networks (RNNs), transformers are designed to process sequential input data, such as natural language, with applications towards tasks such as translation and text summarization. However, unlike RNNs, transformers process the entire input all at once. The attention mechanism provides context for any position in the input sequence. For example, if the input data is a natural language sentence, the transformer does not have to process one word at a time. This allows for more parallelization than RNNs and therefore reduces training times.

Transformers were introduced in 2017 by a team at Google Brain and are increasingly becoming the model of choice for NLP problems, replacing RNN models such as long short-term memory (LSTM). The additional training parallelization allows training on larger datasets. This led to the development of pretrained systems such as BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer), which were trained with large language datasets, such as the Wikipedia Corpus and Common Crawl, and can be fine-tuned for specific tasks.
The paper "Attention is all you need" proposed the Transformer model for the first time, which had a profound impact on the subsequent LLM. Please give the main core idea of Transformer described in this paper
This paper is by Vaswani, Ashish & Shazeer, Noam & Parmar, Niki & Uszkoreit, Jakob & Jones, Llion & Gomez, Aidan & Kaiser, Lukasz & Polosukhin, Illia. and published in 2017. It laid the foundation for the subsequent GPT series of models and BERT series of models. The GPT model uses the decoder part of Transfomer, while the BERT model uses the encoder part of Transfomer. The main core architecture of the Transformer model includes: Self-Attention Mechanism, Positional Encoding, Encoder-Decoder Architecture, Multi-Head Attension.
Transformer effectively solves the difficult parallel problem of the previous RNN/LSTM model by using the Attention mechanism. Using Self-Attention solves the problem of limited labeled data in NLP. The sequence information in the input text is preserved through Positional Encoding. And Multi-Head Attention allows Transformer to learn different patterns of training data similar to Channel concept in CNN model.