Transformers

 

Introduction to the Transformer

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?

What Makes Transformers So Special?

RNN and LSTM models are great for handling sequential data, which is a common task in NLP. However, these models have a hard time capturing long-term dependencies. In simple terms, this means that as a sentence gets longer, the model starts to forget the information from the beginning of the sentence.

To address this limitation, the revolutionary paper, "Attention Is All You Need," introduced the transformer. The transformer is a model that is based entirely on the attention mechanism and gets rid of recurrence. The transformer uses a special type of attention mechanism called self-attention.

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So, How Does It Work?

The transformer model uses an encoder-decoder architecture. Let's use a language translation task to better understand how this works.

First, we feed an input sentence (the source sentence) to the encoder. The encoder then creates a representation of the input sentence and passes it to the decoder. The decoder takes this representation and generates the output sentence (the target sentence).

Imagine you're translating a sentence from English to French. You feed the English sentence into the encoder. The encoder creates a representation of the English sentence and sends it to the decoder. The decoder then takes this representation and generates the French sentence.

But what's happening behind the scenes? What's going on inside the encoder and decoder that allows them to translate a sentence so effectively?

That's exactly what we'll explore in the next post. Stay tuned!

Next Read: https://kavanamlstuff.blogspot.com/2025/08/a-deep-dive-into-attention-mechanism.html