Build your own Transformer from scratch using Pytorch

<p>In this tutorial, we will build a basic Transformer model from scratch using PyTorch. The Transformer model, introduced by Vaswani et al. in the paper &ldquo;Attention is All You Need,&rdquo; is a deep learning architecture designed for sequence-to-sequence tasks, such as machine translation and text summarization. It is based on self-attention mechanisms and has become the foundation for many state-of-the-art natural language processing models, like GPT and BERT.</p> <p>To understand Transformer models in detail kindly visit these two articles:</p> <h2><a href="https://medium.com/towards-data-science/all-you-need-to-know-about-attention-and-transformers-in-depth-understanding-part-1-552f0b41d021" rel="noopener">1. All you need to know about &lsquo;Attention&rsquo; and &lsquo;Transformers&rsquo; &mdash; In-depth Understanding &mdash; Part 1</a></h2> <h2><a href="https://medium.com/towards-data-science/all-you-need-to-know-about-attention-and-transformers-in-depth-understanding-part-2-bf2403804ada" rel="noopener">2. All you need to know about &lsquo;Attention&rsquo; and &lsquo;Transformers&rsquo; &mdash; In-depth Understanding &mdash; Part 2</a></h2> <p>To build our Transformer model, we&rsquo;ll follow these steps:</p> <ol> <li>Import necessary libraries and modules</li> <li>Define the basic building blocks: Multi-Head Attention, Position-wise Feed-Forward Networks, Positional Encoding</li> <li>Build the Encoder and Decoder layers</li> <li>Combine Encoder and Decoder layers to create the complete Transformer model</li> <li>Prepare sample data</li> <li>Train the model</li> </ol> <p>Let&rsquo;s start by importing the necessary libraries and modules.</p> <pre> import torch import torch.nn as nn import torch.optim as optim import torch.utils.data as data import math import copy</pre> <p>Now, we&rsquo;ll define the basic building blocks of the Transformer model.</p> <p><a href="https://towardsdatascience.com/build-your-own-transformer-from-scratch-using-pytorch-84c850470dcb">Website</a></p>