Transformers

Hello, folks! Kiruthika is back after a long break. Yep, let's get started with our Cup of Coffee Series!

Today, we have SpongeBob with us. I am trying to teach him about Transformers.

Haha, finally Today I am having coffee with Spongebob and discussing about transformers!

Kiruthika : Hey Spongebob!

I am excited to have a cup of coffee with you.

Spongebob : Yeah sure, we should. Kiruthika, , tell me one thing: Starbucks, or do you have any other options?

Kiruthika : Hey, wait. Do we discuss Transformers while having coffee? Let's make the coffee choice optional.

Spongebob : Uff....

Oh, like having coffee while discussing Transformers without coffee?

okkkk, sure!

Kiruthika : Let's get started with Transformers!!

In the past few years, the Transformer model has revolutinized our daily life. We are using transformer applications a lot like Bard, ChatGPT.

You Know, Transformer models are incredibly famous and widely used in the field of Artificial Intelligence, particularly in Natural Language Processing (NLP).

Haven't you come across this ad? That's so true.

I said, NLP Applications like Bard, ChatGPT use Transformer models. Here's the question. How these transformer models work?

Well let me explain you, Spongebob!!

Transformer models , which is built on the foundational principle of "attention is all you need," and it have brought a revolution in natural language processing (NLP). By embracing parallel processing, an encoder-decoder structure, and a multi-head attention, self attention mechanism, these models excel at capturing long-range dependencies, which is often a challenge for traditional recurrent models.

Like every search Engine Says!!

umm, I will make it simple.

The Transformer model was introduced in the paper "Attention is All You Need" by Vaswani et al., drew inspiration from earlier deep learning architectures.

While it wasn't a direct adaptation of any particular model, the Transformer's design innovations were influenced by several concepts.

The core innovation of the Transformer, the self-attention mechanism, was inspired by the broader concept of attention in neural networks.

What is Self Attention Mechanism ?

In traditional sequential models, like recurrent neural networks (RNNs), information is processed one word at a time. However, they may struggle with capturing long-range dependencies.

Transformers use self-attention to address this. Instead of processing words in order, they can give different attention weights to all words simultaneously. This parallelization helps capture relationships between words regardless of their position in a sequence.

In simple,

In a transformer, each word in a sentence decides how much attention it should give to other words. These attention levels are like weights, showing how important each word is to the others. During training, the model learns these weights. Then, to understand a sentence, each word combines information from others based on these weights. It's like teamwork among words to grasp the context and meaning of the whole sentence.

Traditional models might struggle with understanding "He went to the bank to deposit money" because they process words sequentially and may get confused between a financial institution (bank) and the side of a river (bank). Transformers, with self-attention, can assign more weight to the word "money," .

Multi-Head Attention:

Rather than relying on a single attention mechanism, transformers use multiple heads in parallel, Each head learns different aspects of the relationships between words, capturing various patterns and dependencies.

So we can call something like, transformers have Multi Head self attention mechanism.

Next,

Haha, let's get into next feature!

The Transformer model can be viewed as a sequence-to-sequence model, a paradigm popularized by earlier architectures like the Encoder-Decoder framework.

First let's understand sequence to sequence models

The basic idea is to use an encoder to process the input sequence and capture its semantic meaning in a fixed-size context vector. This context vector is then used by a decoder to generate the output sequence.

Traditional seq2seq models often used recurrent neural networks (RNNs) for encoding and decoding, but they struggled with capturing long-range dependencies effectively.

Pause......... for a joke

Kiruthika : No worries, I will feed more to your brain, Spongebob!!!

Continued............

But transformers overcame the struggle by self attention mechanism.

The simalirity between sequence to sequence models and Tranformers is

Encoder-Decoder Structure:

The encoder processes the input sequence, transforming it into a series of abstract representations.

The decoder then generates the output sequence based on these representations.

Both encoder and decoder are composed of multiple layers, each containing self-attention mechanisms.

But still we need have more about Transformers!

It addressed limitations in processing long sequences faced by RNNs and LSTMs. While RNNs were effective in sequence modeling, they struggled with parallelization and capturing long-range dependencies. Transformers, with their attention mechanism and parallel processing, overcame these challenges.

lt embraced the concept of parallelizing computations across different regions of data, a strategy successfully employed by Convolutional Neural Networks (CNNs). However, Transformers took this idea a step further by extending it to sequential data.While CNNs work well for grid-like structures such as images, the Transformer adapted the concept to process sequences of words or tokens.

So, other important features of transformers are

1. Transformers lack information about the order of words, positional encodings are added to the input embeddings. So it provide information about the position of each word in the sequence, allowing the model to understand the sequential order.
2. Following the attention layers, transformers include feedforward neural networks, which process the information captured by the attention mechanism

3. Transformers are typically pre-trained on large datasets using unsupervised learning objectives, such as language modeling or masked language modeling. After pre-training, they can be fine-tuned for specific tasks with smaller, task-specific datasets and transfer learning is possible.

Come on let's implement it.

Let us build aTransformer model for sentiment analysis on the IMDb dataset using TensorFlow.

And in this, we are using the sample IMDb dataset in Colab.

This code builds a Transformer model to understand sentiments in movie reviews using the IMDb dataset. Like , a simple transformer model for text classification using TensorFlow.

import tensorflow as tf

# Load the dataset.
dataset = tf.keras.datasets.imdb
(x_train, y_train), (x_test, y_test) = dataset.load_data(num_words=10000)


model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# Train
model.fit(x_train, y_train, epochs=10)

# Evaluate the model.
model.evaluate(x_test, y_test)

print(f"Test Accuracy: {model.evaluate(x_test, y_test)[1] * 100:.2f}%")
        

Hope you got it!!

Ok Spongebob, Keep up Learning. Hope you understood well.

See you soon! Can't wait for the next series.

Cheers,

Kiruthika.