3 Vector-based Methods for Similarity Search (TF-IDF, BM25, SBERT)

Vector similarity search is one of the fastest-growing domains in AI and machine learning. At its core, it is the process of matching relevant pieces of information together. Similarity search is a complex topic and there are countless techniques for building effective search engines. In this video, we'll cover three vector-based approaches for comparing languages and identifying similar 'documents', covering both vector similarity search and semantic search: - TF-IDF - BM25 - Sentence-BERT 📰 Original article: https://www.pinecone.io/learn/semantic-search/ 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Sign-up For New Articles Every Week on Medium! https://medium.com/@jamescalam/membership Mining Massive Datasets Book (Similarity Search): 📚 https://amzn.to/3CC0zrc (3rd ed) 📚 https://amzn.to/3AtHSnV (1st ed, cheaper) 👾 Discord https://discord.gg/c5QtDB9RAP 🕹️ Free AI-Powered Code Refactoring with Sourcery: https://sourcery.ai/?utm_source=YouTub&utm_campaign=JBriggs&utm_medium=aff 00:00 Intro 01:37 TF-IDF 11:44 BM25 20:30 SBERT