How to measure semantic similarity between sentences using modern NLP techniques

Introduction

Have you ever wondered how search engines or chatbots understand that "Machine Learning affects all areas of life" is much more similar to "Artificial intelligence is transforming the world" than "Maradona was one of the best football players in history" ?

This isn't magic — it's embeddings + cosine similarity .

In this blog post, we'll break down exactly how this works, starting from the mathematical foundation and ending with real, runnable Python code using Hugging Face Transformers.

By the end, you'll understand:

What text embeddings actually are

Why cosine similarity is the go-to metric

How to implement semantic text similarity from scratch

Real-world results using the BART model

Let's dive in!

What Are Text Embeddings?

Embeddings are numerical vectors that capture the meaning of text in a high-dimensional space.

Instead of treating words as isolated tokens, modern transformer models (like BERT,…