Liftoff! How to get started with your first ML project 🚀

People who are new to the Machine Learning world often run into two recurring stumbling blocks. The first is choosing the right library to learn, which can be daunting when there are so many to pick from. Even once you’ve settled on a library and gone through some tutorials, the next issue is coming up with your first big project and scoping it properly to maximize your learning. If you’ve run into those problems, and if you're looking for a new ML library to add to your toolkit, you're in the right place!

In this post I’ll take you through some tips for going from 0 to 100 with a new library by using Sentence Transformers (ST) as an example. We'll start by understanding the basics of what ST can do, and highlight some things that make it a great library to learn. Then, I'll share my battle-tested strategy for tackling your first self-driven project. We’ll also talk about how I built my first ST-powered project, and what I learned along the way 🥳

What is Sentence Transformers?

Sentence embeddings? Semantic search? Cosine similarity?!?! 😱 Just a few short weeks ago, these terms were so confusing to me that they made my head spin. I’d heard that Sentence Transformers was a powerful and versatile library for working with language and image data and I was eager to play around with it, but I was worried that I would be out of my depth. As it turns out, I couldn’t have been more wrong!

Sentence Transformers is among the libraries that Hugging Face integrates with, where it’s described with the following:

Compute dense vector representations for sentences, paragraphs, and images

Comparing sentences by similarity means that if we have a collection of sentences or paragraphs, we can quickly find the ones that match a particular search query with a process called semantic search. For some specific applications of this, see this tutorial for making a GitHub code-searcher or this other tutorial on building an FAQ engine using Sentence Transformers.