Transformers, explained: Understand the model behind GPT, BERT, and T5

Summary

This video explores the transformative impact of neural networks known as transformers in the field of machine learning, particularly in natural language processing. Transformers, unlike traditional Recurrent Neural Networks (RNNs), efficiently handle large text sequences and are highly parallelizable, making them ideal for training on vast datasets. Key innovations like positional encodings and self-attention mechanisms enable these models to understand and process language with unprecedented accuracy. The script delves into how transformers work and their applications in models like BERT, which has revolutionized tasks from text summarization to question answering.

Takeaways

• 🌟 Transformers are a revolutionary type of neural network that has significantly impacted the field of machine learning, particularly in natural language processing.
• 🎲 They are capable of tasks such as language translation, text generation, and even computer code generation, showcasing their versatility.
• 🔍 Transformers have the ability to solve complex problems like protein folding in biology, highlighting their potential beyond just language tasks.
• 📈 Popular models like BERT, GPT-3, and T5 are all based on the transformer architecture, indicating its widespread adoption and success.
• 🧠 Unlike Recurrent Neural Networks (RNNs), transformers can be efficiently parallelized, allowing for faster training on large datasets.
• 📚 The transformer model was initially designed for translation but has since been adapted for a wide range of language tasks.
• 📈 Positional encodings are a key innovation in transformers, allowing the model to understand word order without a sequential structure.
• 🔍 The attention mechanism, including self-attention, enables transformers to consider the context of surrounding words, improving language understanding.
• 📈 Self-attention is a crucial aspect of transformers, allowing the model to build an internal representation of language from large amounts of text data.
• 🛠️ BERT, a transformer-based model, has become a versatile tool in NLP, adaptable for tasks such as summarization, question answering, and classification.
• 🌐 Semi-supervised learning with models like BERT demonstrates the effectiveness of building robust models using unlabeled data sources.
• 📚 Resources like TensorFlow Hub and the Hugging Face library provide access to pre-trained transformer models for various applications.

Q & A

What is the main topic of the video script?

The main topic of the video script is the introduction and explanation of transformers, a type of neural network architecture that has significantly impacted the field of machine learning, particularly in natural language processing.

Why are transformers considered revolutionary in machine learning?

Transformers are considered revolutionary because they can efficiently handle various language-related tasks such as translation, text summarization, and text generation. They also allow for efficient parallelization, which enables training on large datasets, leading to significant advancements in the field.

What are some of the limitations of Recurrent Neural Networks (RNNs) mentioned in the script?

The script mentions that RNNs struggle with handling large sequences of text and have difficulty in parallelization due to their sequential processing nature. This makes them slow to train and less effective for large-scale language tasks.

What is the significance of the model GPT-3 mentioned in the script?

GPT-3 is significant because it is a large-scale transformer model trained on almost 45 terabytes of text data, demonstrating the capability of transformers to be trained on vast amounts of data and perform complex language tasks such as writing poetry and code.

What are the three main innovations that make transformers work effectively?

The three main innovations are positional encodings, attention mechanisms, and self-attention. These innovations allow transformers to understand the context and order of words in a sentence, which is crucial for accurate language processing.

What is positional encoding in the context of transformers?

Positional encoding is a method used in transformers to store information about the order of words in a sentence. It assigns a unique number to each word based on its position, allowing the model to understand word order without relying on the network's structure.

Can you explain the concept of attention in transformers?

Attention in transformers is a mechanism that allows the model to focus on different parts of the input data when making predictions. It helps the model to understand the context of words by considering the entire input sentence when translating or generating text.

What is the difference between traditional attention and self-attention in transformers?

Traditional attention aligns words between two different languages, which is useful for translation tasks. Self-attention, on the other hand, allows the model to understand a word in the context of the surrounding words within the same language, helping with tasks like disambiguation and understanding the underlying meaning of language.

What is BERT and how is it used in natural language processing?

BERT (Bidirectional Encoder Representations from Transformers) is a transformer-based model that has been trained on a massive text corpus. It can be adapted to various natural language processing tasks such as text summarization, question answering, and classification.

How can one start using transformer models in their applications?

One can start using transformer models by accessing pre-trained models from TensorFlow Hub or by using the transformers Python library built by Hugging Face. These resources provide easy integration of transformer models into applications.

What is the significance of semi-supervised learning as mentioned in the script?

Semi-supervised learning is significant because it allows for the training of models on large amounts of unlabeled data, such as text from Wikipedia or Reddit. BERT is an example of a model that leverages semi-supervised learning to achieve high performance in natural language processing tasks.

Outlines

00:00

🧠 Introduction to Transformers in Machine Learning

This paragraph introduces the transformative impact of a neural network architecture called 'transformers' in the field of machine learning. Dale Markowitz discusses how these models have revolutionized tasks such as text translation, writing, and even generating computer code. Transformers have made significant strides in natural language processing, with models like BERT, GPT-3, and T5 being based on this architecture. The paragraph sets the stage for an exploration of what transformers are, their functionality, and their impact on the field.

05:01

🌟 The Innovation of Transformer Models

The second paragraph delves into the specifics of transformer models, highlighting their ability to efficiently handle large datasets and complex language tasks. It contrasts transformers with previous models like Recurrent Neural Networks (RNNs), which struggled with long sequences and were difficult to train due to their sequential nature. The paragraph explains the three main innovations of transformers: positional encodings, attention mechanisms, and self-attention. Positional encodings allow the model to understand word order, while attention mechanisms enable the model to consider the context of each word in the sentence. Self-attention is a key feature that allows the model to focus on relevant words in the input text for better understanding and translation. The paragraph also touches on the practical applications of transformers, mentioning models like BERT and the use of semi-supervised learning with unlabeled data.

Keywords

💡Machine Learning

Machine learning is a subset of artificial intelligence that enables computers to learn from and make decisions based on data. In the context of the video, machine learning is the overarching field in which the transformer model operates, revolutionizing tasks such as language translation and text generation. The script mentions how machine learning models have evolved to handle complex data types, with transformers being a significant advancement in this field.

💡Transformers

Transformers refer to a type of neural network architecture introduced in the video as a game-changer in the field of machine learning, particularly for natural language processing tasks. Defined by their ability to efficiently process and understand sequences of data, transformers have enabled the creation of models like BERT and GPT-3. The video emphasizes their versatility and impact, highlighting their use in tasks such as text translation, poetry generation, and even protein folding problem in biology.

💡BERT

BERT, which stands for Bidirectional Encoder Representations from Transformers, is a specific transformer-based model mentioned in the script. It has gained recognition for its ability to understand the context of words within a sentence, making it highly effective for a variety of natural language processing tasks. The video describes BERT as a 'general pocketknife' for NLP, adaptable for tasks such as text summarization and question answering, and its role in Google Search and Google Cloud's NLP tools.

💡GPT-3

GPT-3, or Generative Pre-trained Transformer 3, is another transformer-based model highlighted in the video. Known for its ability to generate human-like text, GPT-3 is an example of how transformers can be trained on vast amounts of data to produce highly realistic and varied outputs, such as poetry and computer code. The script illustrates the capabilities of GPT-3 by referencing its training on nearly 45 terabytes of text data.

💡Natural Language Processing (NLP)

Natural Language Processing is a field of computer science focused on the interaction between computers and human language. The video script emphasizes the importance of NLP in the context of transformers, which have significantly advanced the capabilities of machines to understand, interpret, and generate human language. The discussion around BERT and its applications exemplifies the impact of NLP on tasks such as search query understanding and text summarization.

💡Recurrent Neural Networks (RNNs)

Recurrent Neural Networks, or RNNs, are a class of neural networks that process data sequentially, making them suitable for tasks involving sequences like text. However, the script points out the limitations of RNNs, such as difficulty in handling long sequences and challenges in parallelization, which has led to the development of more efficient models like transformers.

💡Positional Encodings

Positional encodings are a technique used in transformer models to provide information about the relative or absolute position of the tokens in the sequence. The script explains that instead of processing words in a sequential manner as RNNs do, positional encodings allow transformers to consider the order of words by adding a numerical value to each word based on its position in the sentence, which helps the model learn the importance of word order from the data.

💡Attention Mechanism

The attention mechanism is a key innovation in transformer models that allows the model to weigh the importance of different parts of the input data when making predictions. The video script describes how this mechanism enables a model to consider the entire input sequence when translating a word, rather than just the previous words as in RNNs. This leads to more accurate translations and a better understanding of the context.

💡Self-Attention

Self-attention is a specific type of attention mechanism that is central to the transformer architecture. As explained in the script, self-attention allows the model to analyze the input text in relation to itself, which is crucial for understanding the context of words and performing a variety of language tasks. The video provides the example of distinguishing between different meanings of the word 'server' based on the surrounding words.

💡Semi-Supervised Learning

Semi-supervised learning is a machine learning paradigm that utilizes both labeled and unlabeled data for training models. The script mentions BERT as an example of a model that was trained on a large corpus of unlabeled text, such as data scraped from Wikipedia or Reddit, demonstrating the effectiveness of semi-supervised learning in developing advanced NLP models.

💡TensorFlow Hub

TensorFlow Hub is a library and platform for sharing and discovering pre-trained machine learning models, including transformer models like BERT. The video script suggests TensorFlow Hub as a resource for developers to access and integrate powerful transformer models into their applications without the need to train them from scratch.

💡Hugging Face

Hugging Face is a company that has developed a popular Python library for training and using transformer models. The script positions Hugging Face's library as a community favorite for working with transformers, indicating its importance in the ecosystem for natural language processing tasks.

Highlights

Transformers are a revolutionary type of neural network that can perform a variety of language tasks like translation, text generation, and even protein folding.

Popular ML models like BERT, GPT-3, and T5 are all based on the transformer architecture.

Transformers enable efficient parallelization, allowing training on massive datasets like the entire public web.

Positional encodings are used in transformers to encode word order information into the data itself, rather than relying on network structure.

The attention mechanism allows the model to consider the entire input sentence when translating a word, improving translation quality.

Self-attention is a key innovation in transformers, enabling the model to understand words in the context of surrounding words.

Transformers can automatically build an internal representation of language, learning concepts like synonyms, grammar rules, and word tense.

BERT, a transformer-based model, has become a versatile tool for NLP tasks like summarization, question answering, and classification.

BERT demonstrates the power of semi-supervised learning, training on unlabeled text data from sources like Wikipedia and Reddit.

Transformers have a significant impact on machine learning and natural language processing, making them essential to know for anyone in the field.

The transformer architecture addresses limitations of RNNs, such as difficulty handling long sequences and challenges with parallelization.

Transformers can be trained on huge datasets, leading to impressive results and capabilities in language understanding and generation.

The original transformer paper, titled 'Attention Is All You Need', introduced the foundational concepts that have become ubiquitous in machine learning.

Positional encodings and attention mechanisms are the three main innovations that make transformers so effective for language tasks.

Transformers have the potential to solve complex problems in fields beyond language processing, such as biology and the protein folding problem.

TensorFlow Hub and the Hugging Face library provide access to pre-trained transformer models, enabling easy integration into applications.

The video provides an in-depth explanation of how transformers work, their innovations, and their practical applications in the field of AI.

Transcripts

[MUSIC PLAYING]

DALE MARKOWITZ: The neat thing about working

in machine learning is that every few years, somebody

invents something crazy that makes you totally reconsider

what's possible, like models that can play

Go or generate hyper-realistic faces.

And today, the mind-blowing discovery

that's rocking everyone's world is

a type of neural network called a transformer.

Transformers are models that can translate text, write

poems and op-eds, and even generate computer code.

They could be used in biology to solve the protein folding

problem.

Transformers are like this magical machine

learning hammer that seems to make every problem into a nail.

If you've heard of the trendy new ML models

BERT, or GPT-3, or T5, all of these models

are based on transformers.

So if you want to stay hip in machine learning

and especially in natural language processing,

you have to know about the transformer.

So in this video, I'm going to tell you

about what transformers are, how they work,

and why they've been so impactful.

Let's get to it.

So what is a transformer?

It's a type of neural network architecture.

To recap, neural networks are a very effective type

of model for analyzing complicated data

types, like images, videos, audio, and text.

But there are different types of neural networks optimized

for different types of data.

Like if you're analyzing images, you would typically

use a convolutional neural network,

which is designed to vaguely mimic

the way that the human brain processes vision.

And since around 2012, neural networks

have been really good at solving vision tasks,

like identifying objects in photos.

But for a long time, we didn't have anything comparably

good for analyzing language, whether for translation,

or text summarization, or text generation.

And this is a problem, because language is the primary way

that humans communicate.

You see, until transformers came around, the way

we used deep learning to understand text

was with a type of model called a Recurrent Neural Network,

or an RNN, that looked something like this.

Let's say you wanted to translate a sentence

from English to French.

An RNN would take as input an English sentence

and process the words one at a time,

and then sequentially spit out their French counterparts.

The keyword here is sequential.

In language, the order of words matters,

and you can't just shuffle them around.

For example, the sentence Jane went looking for trouble

means something very different than the sentence Trouble

went looking for Jane.

So any model that's going to deal with language

has to capture word order, and recurrent neural networks

do this by looking at one word at a time sequentially.

But RNNs had a lot of problems.

First, they never really did well

at handling large sequences of text, like long paragraphs

or essays.

By the time they were analyzing the end of a paragraph,

they'd forget what happened in the beginning.

And even worse, RNNs were pretty hard to train.

Because they process words sequentially,

they couldn't paralellize well, which

means that you couldn't just speed them up by throwing

lots of GPUs at them.

And when you have a model that's slow to train,

you can't train it on all that much data.

This is where the transformer changed everything.

They're a model developed in 2017 by researchers at Google

and the University of Toronto, and they were initially

designed to do translation.

But unlike recurrent neural networks,

you could really efficiently paralellize transformers.

And that meant that with the right hardware,

you could train some really big models.

How big?

Really big.

Remember GPT-3, that model that writes poetry and code,

and has conversations?

That was trained on almost 45 terabytes of text data,

including almost the entire public web.

[WHISTLES] So if you remember anything about transformers,

let it be this.

Combine a model that scales really well with a huge data

set and the results will probably blow your mind.

So how do these things actually work?

From the diagram in the paper, it should be pretty clear.

Or maybe not.

Actually, it's simpler than you might think.

There are three main innovations that

make this model work so well.

Positional encodings and attention, and specifically,

a type of attention called self-attention.

Let's start by talking about the first one,

positional encodings.

Let's say we're trying to translate text

from English to French.

Positional encodings is the idea that instead

of looking at words sequentially,

you take each word in your sentence,

and before you feed it into the neural network,

you slap a number on it--

1, 2, 3, depending on what number

the word is in the sentence.

In other words, you store information

about word order in the data itself,

rather than in the structure of the network.

Then as you train the network on lots of text data,

it learns how to interpret those positional encodings.

In this way, the neural network learns the importance

of word order from the data.

This is a high level way to understand

positional encodings, but it's an innovation

that really helped make transformers easier

to train than RNNs.

The next innovation in this paper

is a concept called attention, which

you'll see used everywhere in machine learning these days.

In fact, the title of the original transformer paper

is "Attention Is All You Need."

So the agreement on the European economic area

was signed in August 1992.

Did you know that?

That's the example sentence given in the original paper.

And remember, the original transformer

was designed for translation.

Now imagine trying to translate that sentence to French.

One bad way to translate text is to try to translate each word

one for one.

But in French, some words are flipped,

like in the French translation, European comes before economic.

Plus, French is a language that has gendered

agreement between words.

So the word [FRENCH] needs to be in the feminine form

to match with [FRENCH].

The attention mechanism is a neural network structure

that allows a text model to look at every single word

in the original sentence when making

a decision about how to translate a word in the output

sentence.

In fact, here's a nice visualization

from that paper that shows what words in the input sentence

the model is attending to when it

makes predictions about a word for the output sentence.

So when the model outputs the word [FRENCH],,

it's looking at the input words European and economic.

You can think of this diagram as a sort

of heat map for attention.

And how does the model know which words

it should be attending to?

It's something that's learned over time from data.

By seeing thousands of examples of French and English sentence

pairs, the model learns about gender,

and word order, and plurality, and all

of that grammatical stuff.

So we talked about two key transformer innovations,

positional encoding and attention.

But actually, attention had been invented before this paper.

The real innovation in transformers was something

called self-attention, a twist on traditional attention.

The type of attention we just talked about

had to do with aligning words in English and French,

which is really important for translation.

But what if you're just trying to understand

the underlying meaning in language so that you

can build a network that can do any number of language tasks?

What's incredible about neural networks,

like transformers, is that as they analyze tons of text data,

they begin to build up this internal representation

or understanding of language automatically.

They might learn, for example, that the words programmer,

and software engineer, and software developer

are all synonymous.

And they might also naturally learn the rules of grammar,

and gender, and tense, and so on.

The better this internal representation of language

the neural network learns, the better it

will be at any language task.

And it turns out that attention can be a very effective way

to get a neural network to understand language

if it's turned on the input text itself.

Let me give you an example.

Take these two sentences--

Server, can I have the check?

Versus, Looks like I just crashed the server.

The word server here means two very different things.

And I know that, because I'm looking

at the context of the surrounding words.

Self-attention allows a neural network

to understand a word in the context of the words around it.

So when a model processes the word server

in the first sentence, it might be

attending to the word check, which

helps it disambiguate from a human server versus a mail one.

In the second sentence, the model

might be attending to the word crashed to determine

that the server is a machine.

Self-attention can also help neural networks

disambiguate words, recognize parts of speech,

and even identify word tense.

This, in a nutshell, is the value of self-attention.

So to summarize, transformers boil down

to positional encodings, attention, and self-attention.

Of course, this is a 10,000-foot look at transformers.

But how are they actually useful?

One of the most popular transformer-based models

is called BERT, which was invented just around the time

that I joined Google in 2018.

BERT was trained on a massive text corpus

and has become this sort of general pocketknife

for NLP that can be adapted to a bunch of different tasks,

like text summarization, question answering,

classification, and finding similar sentences.

It's used in Google Search to help understand search queries,

and it powers a lot of Google Cloud's NLP tools,

like Google Cloud AutoML Natural Language.

BERT also proved that you could build very good models

on unlabeled data, like text scraped

from Wikipedia or Reddit.

This is called semi-supervised learning,

and it's a big trend in machine learning right now.

So if I've sold you about how cool transformers are,

you might want to start using them in your app.

No problem.

TensorFlow Hub is a great place to grab pretrained transformer

models, like BERT.

You can download them for free in multiple language

and drop them straight into your app.

You can also check out the popular transformers Python

library, built by the company Hugging Face.

That's one of the community's favorite ways

to train and use transformer models.

For more transformer tips, check out

my blog post linked below, and thanks for watching.

[MUSIC PLAYING]