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 “Attention is All You Need,” 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 ‘Attention’ and ‘Transformers’ — In-depth Understanding — 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 ‘Attention’ and ‘Transformers’ — In-depth Understanding — Part 2</a></h2>
<p>To build our Transformer model, we’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’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’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>