hackerllama - Sentence Embeddings

This series aims to demystify embeddings and show you how to use them in your projects. This first blog post will teach you how to use and scale up open-source embedding models. We’ll look into the criteria for picking an existing model, current evaluation methods, and the state of the ecosystem. We’ll look into three exciting applications: You can either read the content here or execute it in Google Colab by clicking the badge at the top of the page. Let’s dive into embeddings! You keep reading about “embeddings this” and “embeddings that”, but you might still not know exactly what they are. You are not alone! Even if you have a vague idea of what embeddings are, you might use them through a black-box API without really understanding what’s going on under the hood. This is a problem because the current state of open-source embedding models is very strong - they are pretty easy to deploy, small (and hence cheap to host), and outperform many closed-source models. An embedding represents

Sentence Embeddings. Introduction to Sentence Embeddings – hackerllama This series aims to demystify embeddings and show you how to use them in your projects. This first blog post will teach you how to use and scale up open-source embedding models. We’ll look into the criteria for picking an existing model, current evaluation methods, and the state of the ecosystem. We’ll look into three exciting applications: Finding the most similar Quora or StackOverflow questions Given a huge dataset, find the most similar items Running search embedding models directly in the users’ browser (no server requ

related reading

• Vector embeddings | OpenAI APIplatform.openai.com
• Embeddings: What they are and why they mattersimonwillison.net
• Vector embeddings | OpenAI APIdevelopers.openai.com
• Introducing text and code embeddings | OpenAIopenai.com
• Comparison of different Word Embeddings on Text Similarity — A use case in NLP | by Intellica.AI | Mediumintellica-ai.medium.com
• The Illustrated Word2vec – Jay Alammar – Visualizing machine learning one concept at a time.jalammar.github.io
• The Illustrated Word2vec – Jay Alammar – Visualizing machine learning one concept at a time.jalammar.github.io
• Meet AI’s multitool: Vector embeddings | Google Cloud Blogcloud.google.com
• Word Embeddingslena-voita.github.io
• Patterns for Building LLM-based Systems & Productseugeneyan.com
• Semantic Search - Sentence Transformers documentationsbert.net
• Transformer Explainer: LLM Transformer Model Visually Explainedpoloclub.github.io