Transformers are the all-in-one solutions for machine learning. They are models that can be created to write poems, articles, translate text, and even manufacture computer codes.
Transformers are similar to AlphaFold 2 in the sense that they can make predictions into proteins based on their genetic sequences. They are also great natural language processing models, just like Switch, BERT, Meena, and T5.
If you plan to be abreast of machine learning, especially in the area of natural language processing, you need to know the basics of Transformers.
In the following paragraphs, we’ll define Transformers, consider how they work, and the impacts they have made.
What is a Transformer?
A Transformer is a kind of neural network architecture that’s used for analyzing complex kinds of data such as audio, videos, texts, and images. Several types of neural networks exist and are optimized for different data types. For instance, convolutional neural networks are optimized for analyzing images in a way that mirrors the human brain processing visual information.
Since 2012, convolutional neural networks have been effective at identifying objects in images, reading digits written by hand, and recognizing faces.
After a long time as the best tools for translations, text generations, summarizations, etc, Transformers have come on the scene to replace convolutional neural networks.
Transformers were developed in 2017 by a team of researchers at Google and the University of Toronto, with the intent to be used for translation. However, the big benefit to Transformers is that they can be parallelized easily and efficiently; meaning that you can develop and train really big models provided you have the right hardware.
When dealing with Transformers, if you‘re able to combine a model that scales well with a huge dataset, you will get amazing, mind-blowing results.
How do Transformers operate?
The workings of transformers are hinged on three primary concepts, which are attention, positional Encodings, and self-Attention.
What are Transformers capable of?
The BERT (Bidirectional Encoder Representations from Transformers) is one very popular Transformer-based model that was introduced by the Google research team in 2018 and was quick to become a part of every natural language processing process, with Google Search not excluded.
BERT is a model of architecture that was trained on a large text corpus by Google researchers, such that it has become an all-purpose NLP solution. It can also be used to solve different tasks, such as classification, question answering, text similarity, text summarization, profane message identification, understanding user questions, etc.
BERT has also made it possible for you to build great language models and train them on data such as texts from Reddit, Quora, and Wikipedia. It has also ensured that large models can be adapted using data specific to one domain for a wide range of uses.
In recent times, the GPT-3 model, a creation of Open Artificial Intelligence, has been getting a lot of attention because it can generate realistic text.
Meena- a chatbot based on Transformers, can hold a compelling conversation on almost all topics, even those that humans consider deep or hard to communicate.
