The Transformer neural network architecture has revolutionized the field of Natural Language Processing (NLP) since its introduction in 2017. It has solved various sequence-to-sequence problems, such as machine translation, summarization, and question-answering tasks. The Transformer architecture employs an encoder-decoder architecture, with a significant difference in the input sequence processing compared to Recurrent Neural Networks (RNNs). The Transformer network can process the entire input sequence in parallel, making it much faster than RNNs. The key concept in the Transformer architecture is self-attention, which allows the network to learn different relationships between words in a sentence. We can build models for various natural language processing tasks with the Transformer. The code snippets provided in this article can be used as a starting point for building Transformer models for various NLP tasks. The Transformer architecture and its variants have revolutionized the natural language processing field and enabled new applications and capabilities that were not possible before.