Models like GPT-3 have an incredible number of parameters enabling it to work this way.
Hard work here is just using a vanilla Transformer approach and throwing massive amounts of data at the model so it performs better. It’s no different with Deep Learning NLP models.
Masking – smart work versus hard work: You can work hard, or you can work smart. So, when you look at another model, you can first look at the tokenizer used and already understand something about that model. How you encode the text has a massive impact on the performance of the model and there are tradeoffs to be made in each decision here. Tokenizers – how these models process text: Models don’t read like you and me, so we need to encode the text so that it can be processed by a deep learning algorithm. With all these models it’s important to understand how they’re different from the Transformer, as that will define which tasks they can do well and which they’ll struggle with. How are BERT and the Transformer Different? BERT uses the Transformer architecture, but it’s different from it in a few critical ways. This has been central to the limits of previous models which could only process text from start to finish. The Transformer architecture enables models to process text in a bidirectional manner, from start to finish and from finish to start. It turns out this is a critical feature of the Transformer architecture. 
Instead, you’re jumping ahead and learning context from the words and letters ahead of where you are right now. You’re not reading this sentence letter by letter in one direction from one side to the other.
The importance of bidirectionality: As you’re reading this, you’re not strictly reading from one side to the other. It still takes time to learn the nuances of each model, but you have a solid foundation and you’re not starting from scratch. Understand that, and RoBERTa or XLNet becomes just the difference between using MySQL or PostgreSQL. The relational model that underpins all of the DBs is the same as the Transformer architecture that underpins our models. It’s the same as when you understand RDBMS technology, giving you a good handle on software like MySQL, PostgreSQL, SQL Server, or Oracle. Understanding the Transformer: You’ve probably heard of BERT and GPT-3, but what about RoBERTa, ALBERT, XLNet, or the LONGFORMER, REFORMER, or T5 Transformer? The amount of new models seems overwhelming, but if you understand the Transformer architecture, you’ll have a window into the internal workings of all of these models.Understanding this is key to seeing why these models have been, and continue to perform well in a range of NLP tasks. One of the key milestones which enabled the rapid evolution in performance was the creation of pre-trained models which could be used “off-the-shelf” and tuned to your specific task with little effort and data, in a process known as transfer learning. NLPs “ImageNet moment pre-trained models: Originally, we all trained our own models, or you had to fully train a model for a specific task.
This way we can understand the limits of previous models and better appreciate the motivation behind the key design aspects of the Transformer architecture, which underpins most SOTA models like BERT.
What did we do before these models? To understand these models, it’s important to look at the problems in this area and understand how we tackled them before models like BERT came on the scene. What is BERT and the transformer, and why do I need to understand it? Models like BERT are already massively impacting academia and business, so we’ll outline some of the ways these models are used, and clarify some of the terminology around them. We will cover ten things to show you where this technology came from, how it was developed, how it works, and what to expect from it in the near future. It’s important to learn about technologies like this, because then you can use them to your advantage. It will end up impacting every part of the modern world. When technology like this comes along, whether it is electricity, the railway, the internet or the iPhone, one thing is clear – you can’t ignore it. In fact, they’re performing so well that people are wondering whether they’re reaching a level of general intelligence, or the evaluation metrics we use to test them just can’t keep up. In recent years language models (LM), which can perform human-like linguistic tasks, have evolved to perform better than anyone could have expected. Few areas of AI are more exciting than NLP right now.
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