A deep dive into this groundbreaking transformer
Contrary to the above image, today we are not going to talk about Bert, the beloved Muppet, and Sesame Street character. Today, we are going to talk about BERT, the groundbreaking Bidirectional Encoder Representations from Transformers that has changed the Artificial Intelligence field.
In this article, we will hopefully simply explain what encoders and self-attention are, just 2 of the words repeatedly mentioned in the article. Along with other necessary contexts.
For those who are new to such a topic and for this article to make some sense, let’s start by identifying what is a Transformer?
When we talk about transformers in the context of NLP and deep learning, we’re not talking about Optimus Prime (although he’s awesome too!). A transformer is a type of neural network architecture that’s been making a big impact in the world of NLP.
In the past, people have used other types of neural networks, like recurrent neural networks (RNNs) and convolutional neural networks (CNNs), for NLP tasks. But these networks had some limitations. That’s where the transformer comes in! The transformer architecture was introduced in the “Attention is All You Need” paper by Vaswani et al. in 2017 and it’s become one of the most popular and widely used architectures in NLP.
What’s so special about the transformer? Well, it uses something called self-attention(we will talk more about it later in the article) mechanisms. This allows the model to weigh the importance of different words in a sentence when making predictions. This makes the transformer really good at handling sequences of data of different lengths and at dealing with long-range dependencies in NLP tasks.
Transformers have been used in a lot of NLP models, including BERT, GPT-2, and many others. These models have achieved some amazing results on a variety of NLP benchmarks and have really transformed the field of NLP.
So, in a nutshell, a transformer in NLP is a type of neural network architecture that uses self-attention mechanisms to process sequences of data effectively in NLP tasks. With that said, what is BERT?
BERT has been making waves in the world of NLP, and for good reason! Created by Google, this model has changed the game in so many ways and has set a new standard for NLP models.
First and foremost, BERT was the first model to introduce pre-training on a massive corpus of text, including the SQuAD (Standform Question Answering Dataset), among others. This approach has completely transformed the way NLP models are developed and trained. Instead of starting from scratch on a smaller task-specific dataset, BERT got to soak up all the language it could handle before it was put to work solving specific NLP tasks. And you know what? This method has become the norm! With transformers allowing for easier model building.
BERT also made history by being the first to use bidirectional representations. This means that it processes the entire sequence of tokens from both ends, giving it a 360-degree view of the context of a word in relation to all other words in the sequence. This was a huge leap forward compared to previous models that could only see one direction.
And that’s not all! Same as all Transformers, BERT also utilizes the attention mechanisms to the NLP party. As stated earlier, this allows the model to focus on different parts of the input sequence when making predictions, ensuring that it accurately captures the relationships between words. With this feature, BERT has seriously stepped up its NLP game.
All of these innovations have allowed BERT to achieve state-of-the-art results on a wide range of NLP benchmarks. It’s no wonder the NLP community is calling it the “BERT Revolution”. BERT has completely changed the way we approach NLP and has set a new standard for models in the field. So, if you haven’t already, it’s definitely time to hop on the BERT bandwagon!
The multi-layer encoder stack in BERT is the backbone of its architecture. The BERT network contains 512 hidden units and 8 attention heads, along with a range of 12 to 24 encoder layers depending on the provided BERT model.
This model is designed to produce a representation of the input sequence that takes into account both the past and future words in the sequence. This is achieved by using several layers of self-attention mechanisms and feed-forward neural networks(residing in the encoder component, more on it later). Each layer takes the representation generated by the previous layer and updates it by first using self-attention to weigh the significance of each word in the sequence. This generates a weighted sum of the representations. Next, the representation is fed into a feed-forward neural network that performs additional transformations to capture more complex relationships between the words. After this step, the output is normalized across the hidden dimensions to make sure the representations from different layers are on the same scale and can be compared easily.
With that said, let’s dive deeper into the above architecture, along with BERT encoders and the self-attention mechanism that makes all of this possible.
Encoder
The encoder is the core component of the BERT model, and it’s responsible for processing the input sequences and generating representations of the text. In reality, the BERT transformer is a bi-directional one. Which means that these encoders are capable of processing the input sequences in both directions, from left to right and from right to left. The data keeps on looping through multiple layers of self-attention mechanisms and feed-forward neural networks that work together to produce a comprehensive representation of the input sequence. Having said that, what does the self-attention mechanism do exactly?
Self-attention
Self-attention is a powerful tool that helps BERT take into account the significance of each word in a sequence when making predictions. This mechanism is crucial to the transformer architecture that is employed in the BERT encoder mentioned above.
In essence, the self-attention mechanism calculates a score for each word in the sequence based on how it relates to all the other words. This score then determines the significance of each word, which is used to produce a combined representation of all the words.
The calculation of the scores is done using three separate matrices: the query matrix, the key matrix, and the value matrix. The query matrix and key matrix are used to calculate the scores, while the value matrix is used to generate the weighted sum of the representations of the words in the sequence. Note that there is also an embedding matrix, which is a matrix of size (vocabulary size, hidden layer size) that contains the word embeddings for each word in the vocabulary.
To calculate the scores, the query representation of each word is first multiplied with the key representation of all the other words in the sequence. The result is a matrix of scores, where each element represents the similarity between the query representation of a word and the key representation of another word.
These scores are then passed through a softmax function to produce a probability distribution over all the words in the sequence. The softmax function is a mathematical function that is commonly used in machine learning, particularly in the field of deep learning. It is used to convert a vector of arbitrary real values to a probability distribution, which is a vector of values that sum to 1. The probabilities are then used to weigh the value representations of the words in the sequence, generating a weighted sum of the representations of all the words in the sequence.
The final representation generated by the self-attention mechanism is then fed into a feed-forward neural network, which further processes the representation and generates the final output representation. This final representation is then passed to the next layer of the transformer, and this process is repeated multiple times until all the layers have been processed.
To sum it up, BERT is a game-changer in the world of natural language processing. Its revolutionary transformer-based architecture, combined with its ability to process input in a bi-directional manner, has set a new standard for NLP models. BERT’s innovative self-attention mechanism allows it to weigh the importance of each word in the input sequence, leading to a more comprehensive representation of the input. This has enabled BERT to achieve state-of-the-art results on a wide range of NLP tasks, and it has rapidly become the go-to model for many researchers and industry practitioners. BERT has paved the way for the development of even more advanced NLP models, and it will continue to drive innovation in the field for years to come.
