Transformer models learn sequences. Learning language sequences is a great place to start considering the billions of messages posted on social media and cloud platforms each day. Consumer behaviors, images, and sounds can also be represented in sequences.

In this lesson, we will first create a general-purpose sequence graph and then build a general-purpose transformer-based recommender in Jupyter notebook. We will then see how to deploy them in metahumans.

Let’s first define general-purpose sequences.

General-purpose sequences

Many activities can be represented by entities and links between them. They are therefore organized in sequences. For example, a video on YouTube can be an entity A, and the link can be the behavior of a person going from video A to video E.

Another example is a bad fever being an entity F, and the link being the inference a doctor may make leading to a micro-decision B. The purchase of product D on Amazon by a consumer can generate a link to a suggestion, C, or another product. The examples are infinite!

We can define the entities in this section with six letters:

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