Search⌘ K
Home
Courses
Getting Started with Google BERT

Introduction to the Transformer

Explore the fundamental concepts of the transformer architecture used in natural language processing. Understand how its encoder-decoder structure and self-attention mechanism enable improved language translation and set the foundation for models like BERT and GPT. This lesson helps you grasp why transformers replaced older models like RNNs and LSTMs and how they handle long-term dependencies in sequential data.

We'll cover the following...

The transformer is one of the most popular state-of-the-art deep learning architectures that is mostly used for natural language processing (NLP) tasks. Ever since the advent of the transformer, it has replaced the recurrent neural network (RNN) and long short-term memory (LSTM) for various tasks. Several new NLP models, such as BERT, GPT, and T5, are based on the transformer architecture.

Why transformer?

RNN and LSTM networks are widely used in sequential tasks such as next word prediction, machine translation, text generation, and more. However, one of the major challenges with the recurrent model is capturing the long-term dependency.

To overcome this limitation of RNNs, a new architecture called Transformer was introduced in the paper “Attention Is All You NeedVaswani, Ashish, Google Brain, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan Gomez, Łukasz Kaiser, and Illia Polosukhin. n.d. “Attention Is All You Need.” https://papers.nips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf ‌.” The transformer is currently the state-of-the-art model for several NLP tasks. The advent of the transformer created a major breakthrough in the field of NLP and also paved the way for new revolutionary architectures such as BERT, GPT-3, T5, and more.

The transformer model is based entirely on the attention mechanism and completely gets rid of recurrence. The transformer uses a special type of attention mechanism called self-attention.

How does the transformer work

Let's understand how the transformer works with a language translation task. The transformer consists of an encoder-decoder architecture. We feed the input sentence (source sentence) to the encoder. The encoder learns the representation of the input sentence and sends the representation to the decoder. The decoder receives the representation learned by the encoder as an input and generates the output sentence (target sentence).

Suppose we need to convert a sentence from English to French. We feed the English sentence as input to the encoder. The encoder learns the representation of the given English sentence and feeds the representation to the decoder. The decoder takes the encoder's representation as input and generates the French sentence as output:

Encoder and decoder of the transformer
Encoder and decoder of the transformer

Okay, but what's exactly going on here? How does the encoder and decoder in the transformer convert the English sentence (source sentence) to the French sentence (target sentence)? What's going on inside the encoder and decoder?