Learn how to ensure the quality of your embeddings, which can be essential for your machine-learning system.
Creating quality embeddings is an essential part of most AI systems. Embeddings are the foundation on which an AI model can do its job, and creating high-quality embeddings is, therefore, an important element in making high-accuracy AI models. This article will talk about how you can ensure the quality of your embeddings, which can help you create better AI models.
First of all, embeddings are information stored as an array of numbers. This is typically required when you are using an AI model, as the AI models only accept numbers as input, and you cannot for example feed text straight into an AI model to do NLP analysis. Creating embeddings can be done with several different approaches like autoencoders or from training on downstream tasks. The problem with embeddings however is that they are meaningless to the human eye. You cannot judge the quality of an embedding by simply looking at the numbers, and measuring the quality of the embeddings in general can be a challenging task. Thus, this article will explain how you can get an indication of the quality of your embedding, though these methods unfortunately cannot guarantee the quality of the embeddings, considering this is a challenging task.
· Introduction
· Table of contents
· Dimensionality reduction
∘ Qualitative approach
∘ Quantitative approach
∘ When to use dimensionality reduction
∘ When not to use dimensionality reduction
· Embedding similarity
∘ When to use embedding similarity
∘ When not to use embedding similarity
· Downstream tasks
∘ When to use downstream tasks
∘ When not to use downstream tasks
· Improving your embeddings
∘ Open-source models
∘ Check for bugs
· Conclusion
· References
