What is a Transformer Model?

What Is a Transformer Model?

This kind of Neural Network is typically used to process long sequences of data and determine the relationships between them. In Natural Language Processing (NLP) this architecture is used in preference to Recurrent Neural Networks as it processes information in a significantly different way.

This neural network architecture was first introduced in the research paper Attention Is All You Need from 2017. The core idea behind the Transformer architecture is the use of so-called attention mechanisms as opposed to recurrence. Attention allows the model to look up and integrate information from other parts of the sequence when predicting a single part of the output sequence.

In Natural Language Processing, input data is split into smaller parts, called tokens and then turned into numbers and analyzed in terms of context between tokens to produce output based on the model’s architecture.

While early Transformer architectures were developed specifically for natural language processing (i.e. language), many variations of the architecture have since been created to apply to other data types such as images, audio, speech, and even code. As such, there are many modern AI architectures that are built using some form of Transformer architecture.

How Does a Transformer Model Work?

First, the input information is put into a form that can be processed by the neural network. If for example, the application is for language, then each token in the input text is converted to a numerical embedding.

In addition to embedding the input tokens in vector space, Transformer Model also needs to know the position of each token in sequence. Because Transformer Model processes multiple tokens in parallel, it is not like a recurrent model that “naturally” reads input from left to right. Instead, the model must be informed of the position of each token.

The attention is the core of the architecture. It allows the model to decide which parts of the input are most relevant to each other. In other words, it allows a word near the beginning of a sentence to influence the meaning of words in the end of the sentence, even if they are separated by many other words.

For instance, in very long sentences a word close to the beginning can have a big impact on words at the end of the sentence. The model must be able to take into account these types of relationships between words.

After self-attention, other layers can be applied on the resulting representation, and this process can be repeated to increase the depth of the Transformer.

Note that the output space of the final architecture can be a classification, a single prediction, or even a complete translated sentence, or generated response.

What Is Attention in a Transformer?

The name “attention” is actually a misleading label. Attention doesn’t mean paying attention to something. It simply means assigning different weights to different parts of the input. So attention in the Transformer can be seen as the computation that decides which parts of the input are most relevant to other parts.

The ability to model long sequences is especially relevant for tasks where input information is spread over a long sequence of words. The model can easily identify relevant words that are far away from each other in the input.

Self-Attention

The self-attention mechanism in a Transformer enables tokens from within a single input sequence to interact with each other. In the previous example, the word it can benefit from considering words that are elsewhere in the sentence, such as the beginning of the sentence.

In Self-Attention, a single input token can attend to the entire sequence to determine relevance. In the following example, a pronoun such as “they” or “it” in a sentence can attend to the rest of the words in the earlier part of the sentence to get the meaning of the pronoun.

The Self-Attention mechanism typically takes in three types of information (or representations) for a single token in the input sequence: the query, key, and value representations. The query will query other tokens in the sequence for relevant information, the key will be used to match the query against, and the value will be used to pass the relevant information along after the attention scores have been calculated.

Multi-Head Attention

This kind of attention is often used in several attention modules in parallel, which is called multi-head attention.

As these heads process information of different relationships (in terms of depth), their outputs are mixed up and fed into subsequent layers of the model. Typically, the relationships that are close (i.e. near each other) are grammatical in nature and can be found with the first few heads, whereas the relationships that are far apart are conceptual in nature and are found by the latter heads.

The outputs from the individual attention heads are then combined in subsequent layers, enabling the model to gain a more in-depth understanding of the data in the input.

What Are the Main Components of a Transformer?

A Transformer is composed of many components to process information.

Tokenization: The input text is first tokenized (i.e. it is split into smaller units, like individual words or characters) and then each token is mapped to a numerical vector (its embedding).

The position of each token in a sequence is also important for a Transformer and thus needs to be encoded. The way a Transformer is dealing with sequential information is by processing it in parallel.

The main part of the Transformer are the attention mechanisms, which are used to extract information from parts of the input. The central aspect of this is to establish relationships between tokens in a sequence.

A feed-forward neural network is used to process the attention output in more complexity. Every Transformer layer contains an attention module and a feed-forward module, normalization and (residual) connections.

Note that some models may include encoders, decoders, and others in between to process input information and generate corresponding output.

What Are the Main Types of Transformer Architectures?

There are three main forms of Transformer models. These are known as encoder-only models, decoder-only models and encoder-decoder models.

Encoder-Only Transformers

These Transformer Models are typically used for tasks that aim to understand input information and create representations. For example, classify, extract information, analyze sentiment, etc.

The encoder-based Transformer architecture (such as that used in BERT) is used to try and understand the full meaning of a piece of text.

Decoder-Only Transformers

The Decoder-only Transformer is similar to the Encoder-only Transformer and is used to generate sequences. It predicts the next token in the sequence given all the previous tokens in the sequence.

The model is particularly suited for generation one token at a time and hence is used in the majority of modern language generation systems including very large language models.

Encoder-Decoder Transformers

An encoder-decoder Transformer consists of two sub-models: an encoder and a decoder. The encoder first reads in a given input, and then the decoder uses this input to generate an output.

The encoder-decoder structure is useful when one sequence must be transformed into another. This is especially true for machine translation where the encoder feeds the source language to the decoder who then generates the translated target language sequence.

Where Are Transformer Models Used?

There are many applications of the Transformer Models today. Their ability to handle large amounts of data and to find connections between them makes them not only suitable for Natural Language Processing but also for many other areas of AI.

Natural Language Processing

Many of the current applications of Transformer Models are in Natural Language Processing (NLP) to perform text classification, text summarization, question answering, information retrieval, machine translation and text generation.

Instead of only looking at individual words, the model can also look at words before and after a certain word. This way, the model is able to better understand the language and use it in different situations.

Machine Translation

A typical machine translation system uses an encoder-decoder architecture and is based on the Transformer model. The encoder processes the source language sentence and the decoder generates a target language sentence.

The model uses the attention mechanism to decide which parts of the source sentence to use when generating each part of the target translation.

Conversational AI

Conversational AI systems like chatbots and virtual assistants often use a Transformer-based model to read user input and then generate the correct response.

This architecture enables the system to consider previous messages in a conversation when generating an appropriate response. The meaning of a message can often depend on previous messages in a conversation.

Generative AI

Generative AI using the Transformer architecture. Generative AI can create content such as text, code and more. In Text Generation a Decoder-Based Transformer is used.

Again, it’s often the decoder-based Transformer models that are used most for text generation in order to output a sequence of tokens, one at a time, in order, until it runs out of tokens to produce.

Computer Vision

Transformers have also been adapted for computer vision.

The Vision Transformer (ViT) model represents parts of an image as a sequence of tokens. The model then uses attention in order to understand relationships between parts of the image in the process of image classification as well as in other computer vision tasks.

Speech and Audio

Transformer architectures are also widely used in speech recognition, in speech and audio transcription, and in audio and other types of media processing and analysis.

Speech and audio information also is sequential in nature, so attention can be effective to identify relationships between parts of an audio signal.

Code Generation

Transformer-based models are able to process programming languages of all kinds in a similar manner to how they process human languages.

Code Generation models can assist with code completion, code generation, code explanation, code documentation, cross-language code translation and even bug fixing. Again, the model’s ability to assist will depend on the model itself and the training data used to train the model.

What Are the Benefits of Transformer Models?

A key feature of Transformer architecture is the ability to capture long-distance relations in a sequence. As already mentioned, the attention mechanism allows a model to connect tokens that are far away from each other.

Because of the way attention is implemented in the Transformer, it’s very good at handling a word’s dependence on information that appears far before it in the paragraph—information that in a traditional sequence model would fade away by the time the word is processed.

These models are well suited for parallel processing of tokens, within a sequence, and thus can make effective use of large amounts of computational resources to train extremely large models.

Scalability is another strong point of the Transformer architecture. As more data becomes available for training models, larger models with more parameters can be used. Again, language and foundation models are the prime examples of this development.

It is also very flexible. The architecture is typically applied to text, but can also be used for images, audio, and code. So the Transformer architecture is not limited to the original application of language processing, but can also be applied to a variety of other tasks and data types.

In addition to training a model from scratch, a model can also be used for transfer learning. A pre-trained model can be fine-tuned on a specific task and in many cases even on a specific dataset. This saves a lot of training time, as not the whole model has to be trained from scratch again.

What Are Some Examples of Transformer-Based Models?

There are a number of well known models that are based on the Transformer architecture, such as BERT, GPT, T5, and others. Each of these models use a slightly different version of the original architecture in order to best meet the requirements of the tasks that they were designed to complete.

BERT: This is an example of an encoder-based Transformer model, for understanding language. It is used for many kinds of language models including classification models, question-answering models and others for information-extraction tasks.

GPT is a class of generative models for language use decoder-based Transformer architectures, for generating natural language texts that can be used for language understanding and many generative applications.

The T5 model is based on the Text-to-Text Transfer Transformer, an encoder-decoder architecture that can transform input text of any length and form into output text, whether it is another text, the classification of input text, or anything else.

Vision Transformer (ViT): The Transformer is also applied to computer vision in the form of the Vision Transformer (ViT) which takes image regions as input and processes them in a sequence applying attention to select relevant information.

We have tried to provide a few examples of models based on the Transformer architecture. Many other models such as those for speech, for code, for multimodal data, etc., have been designed and are modifications of the basic architecture for the required data and corresponding tasks.