Demystifying Transformer models: A theoretical Exploration

In today world where all the things is changing rapidly, we as an engineer try to breakdown the reason behind it. One of the things which have created hype is transformer model and so, we are going to explore transformer models, and understand its hype is real or not ?

Basics of Transformer Models:

Transfer models are class of Deep Learning and it is designed in such a way to process sequential data(order of elements matters) more efficiently than previous models like recurrent neural network(RNNs) and long-short term memory(LSTM) network.

Now, we are going to explore components of transfer models, but before that we have to study about self-attention mechanism which play a key role in understanding components.

Self-Attention mechanism:

The key innovation behind transformer is self-attention mechanism, which provide the ability to model to focus on different part of input sequence simultaneously. Unlike RNNs, which read one word at a time, transformers process all word in parallel. This makes them much faster and more efficient, especially with large amount of data.

It is not only popular for performance but for their flexibility also. Unlike LSTMs, they don't depend on word during processing, instead they use something called positional encoding(which we will see in components) to understand the position of each word, which helps in building relationships between words.

Components of Transformer Models:

There are many components of transformer model, which we are going to study:

1. Positional Encoding: We know transformer models process data in parallel, so we have to understand the order of sequence of data, and here positional encoding help us to maintain it.

2. Multi Head Attention Mechanism: This has two main types: self-attention and cross-attention:

a. Self-Attention: It helps the model to focus on important parts within the same sequence. It used in all type of transformer models-encoder only, decoder only, and encoder-decoder.

b. Cross-Attention: It helps the model to focus on parts of another sequence. This type of attention is used only in encoder-decoder models like those model used in translation.

3. Feed Forward Layers: After the attention mechanism (like self-attention or cross-attention), the output goes through a Feed Forward Neural Network(FFN). This is the same for each position in the sequence.

4. Linear:

   1. Each attention head has three linear layers:

      1. Query (Q)

      2. Key (K)

      3. Value (V)

   2. Each Transformer block has a 2-layer feed-forward network:

• First linear layer expands the dimension (e.g., from 768 → 3072)

• Second linear layer brings it back (e.g., from 3072 → 768)

This lets the model transform and refine the information learned during attention.

5. Softmax:

   1. In the Attention Mechanism:

• After computing attention scores (dot product of queries and keys), softmax turns those scores into attention weights — probabilities that show how much focus to give to each token.

2. In the Output Layer:

• In models like BERT or GPT, softmax is used on the final logits to convert them into predicted probabilities over the vocabulary.

Benefits of Transformer models:

1. Parallelization

Unlike RNNs, Transformers process all tokens at once — not step-by-step — allowing for faster training using GPUs.

2. Long-Range Dependencies

The self-attention mechanism helps capture relationships between far-apart tokens in a sequence better than LSTMs or GRUs.

3. Scalability

Transformers scale well with large datasets and model sizes. This is why models like BERT, GPT, and T5 can be trained with billions of parameters.

4. Versatility

Same architecture can be used for:

• Text (translation, summarization)

• Code (code generation)

• Images (Vision Transformers)

• Audio (speech recognition)

5. Better Context Understanding

Thanks to multi-head self-attention, Transformers can focus on multiple parts of the input simultaneously, improving contextual understanding.

Application of Transformer models:

1. Natural Language Processing (NLP)

• Text Classification
  (e.g., sentiment analysis, spam detection) — using models like BERT

• Machine Translation
  (e.g., English to French) — Transformer was originally designed for this

• Question Answering
  (e.g., SQuAD dataset tasks) — using BERT

• Text Summarization
  (e.g., news article to short summary) — using models like T5 or BART

2. Vision (Computer Vision)

• Image Classification & Object Detection using Vision Transformers (ViT)

• Image Captioning (combining visual input with language output)

3. Audio and Speech

• Speech Recognition (e.g., converting voice to text)

• Text-to-Speech (TTS) (e.g., generating human-like voices from text)