Hands-on: Utilizing Embeddings for Text Similarity
Explore how to create and use text embeddings with the OpenAI API to measure semantic similarity between text strings. This lesson guides you through generating embeddings, calculating cosine similarity scores, and interpreting those results. Gain hands-on experience to understand how embeddings can quantify relationships between phrases, helping you apply this in clustering and other NLP projects.
We'll cover the following...
Since we have learned the concepts behind embeddings, text similarity, and clustering, it's time to put our knowledge to the test. In this section, we will compute embeddings for a dataset and then cluster them based on the scores.
Text similarity project
We'll explain each section and then put it all together at the end for you to try it out.
Note: To perform embeddings on your local machine, please first install the openai and openai[datalib] libraries as follows:
pip install openai
pip install openai[datalib] ...