Transformers: A Comprehensive Pure Beginner's Guide

Imagine you are trying to teach a computer to translate a sentence from English to French. One way to do this would be to give the computer a dictionary of all the English and French words, and then tell it to translate each word in the sentence one by one.

However, this would not work very well, because it would not take into account the context of the words in the sentence.

For example, the English word "bank" can mean different things in different contexts. It could mean a financial institution, or it could mean the side of a river. In order to translate the word correctly, the computer needs to know which meaning is intended.

Transformers work by learning to represent the context of words in a sentence. They do this by using a technique called self-attention. Self-attention allows the transformer to attend to different parts of the sentence, depending on what it is trying to do.

For example, when the transformer is translating the word "bank", it will attend to the other words in the sentence to determine which meaning of the word is intended. If the sentence is "I went to the bank to deposit my money", then the transformer will attend to the words "deposit" and "money" to determine that the meaning of "bank" is a financial institution.

Transformers have been shown to achieve state-of-the-art results on a wide range of natural language processing tasks. They are also being used in other areas of machine learning, such as computer vision and speech recognition.

Okkkk, Here is another simple analogy that you can use to explain transformers

Imagine that you are trying to help a friend with their homework. Your friend is trying to write a paragraph about a historical event, but they are having trouble with the order of events.

You could try to help your friends by telling them the order of events one by one. However, this would be a bit inefficient, because it would not take into account the relationships between the events.

For example, if the first event is the signing of a declaration of war, and the second event is the start of a battle, then it is important to know that the second event happened after the first event.

A better way to help your friend would be to give them a map of the events. The map would show the order of the events, as well as the relationships between them.

Time for some technical

Transformers work in a similar way. They learn to represent the relationships between words in a sentence, which allows them to perform tasks such as machine translation and text summarization. In this article, I'll try to cover the below points.

• What is a transformer?
• The history of transformers
• The advantages of transformers
• The architecture of transformers
• The encoder
• The decoder
• The self-attention mechanism
• The multi-head attention mechanism
• The loss function
• The optimizer
• The hyperparameters
• The training of transformers
• Applications of transformers
• More Real Life examples
• Conclusion

Introduction

A transformer is a type of neural network architecture that has become the dominant approach for natural language processing tasks.

Transformers are based on the attention mechanism, which allows them to learn long-range dependencies between words in a sentence. This makes them well-suited for tasks such as machine translation, text summarization, and question-answering.

The transformer architecture is inspired by the human brain's ability to focus on relevant information and ignore irrelevant information.

The self-attention mechanism allows the transformer to learn which words in a sentence are most important for understanding the meaning of the sentence.

The multi-head attention mechanism allows the transformer to learn different aspects of the meaning of a sentence.

The history of transformers

The idea of using attention in neural networks was first proposed in the 1990s. However, it was not until the development of deep learning that attention mechanisms became practical.

The first transformer model was proposed in 2017, and it quickly achieved state-of-the-art results on a variety of natural language processing tasks.

The advantages of transformers

Transformers have several advantages over other neural network architectures for natural language processing tasks.

First, they are able to learn long-range dependencies between words. This is because the self-attention mechanism allows the transformer to attend to any word in the sentence, regardless of its position.

Second, transformers are able to parallelize the computation of attention, which makes them much faster to train than other neural network architectures.

The architecture of transformers

A transformer consists of two main parts: the encoder and the decoder. The encoder takes a sequence of input tokens, such as words, and produces a sequence of hidden states.

The decoder then takes these hidden states and produces a sequence of output tokens. The encoder and decoder are both made up of a stack of self-attention layers and feed-forward layers.

The self-attention layers allow the transformer to learn which words in the input sequence are most important for understanding the meaning of the sentence. The feed-forward layers allow the transformer to learn more complex relationships between the words.

The encoder

Imagine you have a book and you want to write a summary of it. The encoder is like the first part of your brain that reads the book and tries to understand what it's about. It does this by breaking the book down into smaller and smaller pieces, like words, sentences, and paragraphs. Then, it tries to figure out how all of these pieces fit together to tell the story.

In technical terms, the encoder is the first part of a transformer model. It takes a sequence of words or other input tokens and converts them into a sequence of hidden states. These hidden states represent the meaning of the input sequence, and they are used by the decoder to generate the output sequence.

The decoder

The decoder is like the second part of your brain that takes the encoder's understanding of the book and generates a summary. It does this by starting with a single word or phrase, and then adding more words and phrases until it has created a complete summary.

In technical terms, the decoder is the second part of a transformer model. It takes the hidden states from the encoder and generates a sequence of output tokens. The decoder can generate any type of sequence, such as text, code, or music.

The self-attention mechanism

The self-attention mechanism is a special way that the encoder and decoder pay attention to different parts of the book. It allows them to focus on the most important parts of the book, and to ignore the less important parts.

Technically it allows the encoder and decoder to attend to different parts of the input and output sequences, respectively. This is important for understanding long sequences, such as sentences and paragraphs.

The multi-head attention mechanism

The multi-head attention mechanism is a more powerful version of the self-attention mechanism. It allows the encoder and decoder to pay attention to different parts of the book in different ways. This makes it easier for them to understand complex books and to generate accurate summaries.

For example, Imagine you are trying to write a poem. You might think about the poem from different perspectives, like the rhyme scheme, the meter, and the overall meaning of the poem. This is what the multi-head attention mechanism does. It allows the encoder and decoder to attend to different parts of the input sequence from different perspectives, which helps them to learn more complex relationships in the sequence.

Here is a simplified explanation of how transformers work:

1. The encoder reads the input and tries to understand what it's about.
2. The self-attention mechanism helps the encoder to focus on the most important parts of the input.
3. The decoder generates an output based on the encoder's understanding of the input.
4. The multi-head attention mechanism helps the decoder to pay attention to different parts of the input in different ways.

The Loss Function

The loss function is a measure of how well a machine learning model is performing on a given task. It is calculated by comparing the model's predictions to the actual values.

The goal is to minimize the loss function, which means that the model should make as few mistakes as possible.

For example, let's say we are training a machine learning model to predict the price of a house. The loss function could be the difference between the predicted price and the actual price. If the model predicts that a house will cost $100,000 but it actually costs $200,000, then the loss function will be $100,000.

Let's take another example; Imagine you are playing a game with your friend. You are trying to guess the number your friend is thinking of. Your friend gives you a hint, and you guess a number. If your guess is close to the actual number, you get a lot of points. But if your guess is far away from the actual number, you get fewer points.

The loss function in transformers is similar. It measures how close the model's predictions are to the actual values. The goal is to minimize the loss function so that the model's predictions are as accurate as possible.

Let's say we are training a transformer model to predict the next word in a sentence. The model is given the sentence "I love to eat apples.", and it predicts the next word as "oranges.".

The actual next word is "apples," so the model's prediction is incorrect. The loss function will calculate how incorrect the prediction is.

One common loss function is the cross-entropy loss. The cross-entropy loss is calculated as follows:

loss = -sum(p_true * log(p_pred))

where:

• p_true is the probability of the actual next word
• p_pred is the probability of the predicted next word

In this case, the cross-entropy loss would be calculated as follows:

loss = -sum(1 * log(0.5))

This gives a loss of 0.69.

The optimizer

The optimizer is like a coach in a sports team. It helps the model improve its performance by updating the model's parameters.

The optimizer looks at the loss function and figures out which parameters need to be changed to reduce the loss. It then updates the parameters accordingly.

One common optimizer is the Adam optimizer. The Adam optimizer updates the parameters as follows:

parameter = parameter - learning_rate * (gradient / adam_weight)

where:

• parameter is the parameter to be updated
• learning_rate is the learning rate hyperparameter
• gradient is the gradient of the loss function with respect to the parameter
• adam_weight is a hyperparameter that controls how much the gradient is smoothed

In this case, the Adam optimizer would update the model's parameters to reduce the cross-entropy loss.

The hyperparameters

Hyperparameters are like the rules of a game. They control how the training process works.

Some common hyperparameters include the learning rate, the batch size, and the number of epochs.

• The learning rate hyperparameter controls how much the model's parameters are updated at each step. A higher learning rate will cause the model to learn more quickly, but it may also cause the model to overfit the training data.
• The batch size hyperparameter controls how many examples are used to update the model's parameters at each step. A larger batch size will cause the model to learn more slowly, but it may also help to reduce overfitting.
• The number of epochs hyperparameter controls how many times the model sees the entire training set. A higher number of epochs will cause the model to learn more thoroughly, but it may also take longer to train.

It is important to tune the hyperparameters to find the best values for the specific problem and the model being used. The hyperparameters are typically chosen by trial and error.

Demo calculation

Let's say we are training a transformer model to predict the next word in a sentence using the Adam optimizer and the cross-entropy loss function. We have set the learning rate to 0.01 and the batch size to 16.

The model is given the sentence "I love to eat apples.", and it predicts the next word as "oranges.".

The cross-entropy loss is calculated as follows:

loss = -sum(1 * log(0.5))

This gives a loss of 0.69.

The Adam optimizer will then update the model's parameters to reduce the loss.

The updated parameters will be used to make predictions on the next batch of examples. The loss will be calculated again, and the Adam optimizer will update the parameters again.

This process will continue until the model reaches a certain number of epochs (hyperparameter) or until the loss function falls below a certain threshold

The training of transformers

Transformers are trained using a supervised learning approach. This means that the transformer is given a set of input-output pairs, and it is trained to predict the output given the input. The loss function for training transformers is typically the cross-entropy loss.

The optimizer for training transformers is typically the Adam optimizer. The Adam optimizer is a stochastic gradient descent optimizer that has been shown to be effective for training deep learning models.

Applications of transformers

Transformers have been used to achieve state-of-the-art results on a wide variety of NLP tasks. Here are a few examples:

Machine translation: Transformers have been used to improve the accuracy of machine translation systems by a significant margin. For example, the transformer-based model Google Translate uses is now able to translate languages with a high degree of accuracy.

Text summarization: Transformers can be used to automatically summarize long pieces of text. This can be useful for tasks like creating news summaries or generating product descriptions.

Question answering: Transformers can be used to answer questions about text. This can be useful for tasks like helping students with their homework or providing customer support.
Transformers have also been applied to other tasks, such as computer vision and music generation.

Real-Life Examples:

Example 1: Using Transformers to Translate Languages

Let's take a look at a real-life example of how transformers can be used to translate languages. Imagine that you want to translate a sentence from English to Spanish. You could use a traditional machine translation system, but it would probably not be very accurate.

However, if you use a transformer-based machine translation system, you can get much better results.

The transformer-based machine translation system would first encode the English sentence into a sequence of vectors. Then, it would use the self-attention mechanism to learn how each word in the sentence relates to each other. Finally, it would decode the encoded sequence into the Spanish sentence.

The transformer-based machine translation system would be able to learn that the English word "dog" is related to the Spanish word "perro". It would also be able to learn that the English word "cat" is related to the Spanish word "gato". This would allow the system to translate the English sentence "The dog chased the cat" into the Spanish sentence "El perro persiguió al gato".

Example 2: Journalism summarization

Imagine that you are a journalist working on a story about the refugee crisis. You have been given access to a large dataset of text and code, including transcripts of interviews with refugees, news articles, and social media posts.

You want to use this data to create a machine learning model that can automatically generate summaries of refugee stories.

You decide to use a transformer model for this task. Transformers are well-suited for NLP tasks that require understanding long-range dependencies, such as summarizing text. You train the model on the dataset of text and code, and then you use it to generate summaries of refugee stories.

The summaries are very accurate and informative. They capture the main points of the stories, and they also convey the emotions and experiences of the refugees.

You are able to use these summaries to create a powerful and moving story about the refugee crisis.

Conclusion

The transformer is like a human brain, able to focus on relevant information and ignore irrelevant information.

The self-attention mechanism is like a spotlight, allowing the transformer to focus on specific words in a sentence.

The multi-head attention mechanism is like a magnifying glass, allowing the transformer to zoom in on different aspects of the meaning of a sentence.

I hope this article has given you a better understanding of transformers. They are a powerful tool that can be used for a variety of tasks in machine learning.

I encourage you to learn more about them and to explore their potential applications.

Hi, I am Rajan Verma, Thanks for coming so far, if you enjoy reading this, do consider checking my profile. for more stuff.