Understanding Transformers, the Data Science Way. Read this accessible and conversational article about understanding transformers, the data science way — by asking a lot of questions that is.
Transformers have become the defacto standard for any NLP tasks nowadays. Not only that, but they are now also being used in Computer Vision and to generate music. I am sure you would all have heard about the GPT3 Transformer and its applications thereof. But all these things aside, they are still hard to understand as ever.
It has taken me multiple readings through the Google research paper that first introduced transformers along with just so many blog posts to really understand how a transformer works.
So, I thought of putting the whole idea down in as simple words as possible along with some very basic Math and some puns as I am a proponent of having some fun while learning. I will try to keep both the jargon and the technicality to a minimum, yet it is such a topic that I could only do so much. And my goal is to make the reader understand even the goriest details of Transformer by the end of this post.
_Also, this is officially my longest post both in terms of time taken to write it as well as the length of the post. Hence, I will advise you to Grab A Coffee. _☕️
So, here goes — This post will be a highly conversational one and it is about “Decoding The Transformer”.
Q: So, Why should I even understand Transformer?
In the past, the LSTM and GRU architecture(as explained here in my past post on NLP) along with attention mechanism used to be the State of the Art Approach for Language modeling problems (put very simply, predict the next word) and Translation systems. But, the main problem with these architectures is that they are recurrent in nature, and the runtime increases as the sequence length increases. That is, these architectures take a sentence and process each word in a sequential way, and hence with the increase in sentence length the whole runtime increases.
Transformer, a model architecture first explained in the paper Attention is all you need, lets go of this recurrence and instead relies entirely on an attention mechanism to draw global dependencies between input and output. And that makes it FAST.
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