What are Contextual Embeddings?
Embeddings that capture the meaning of words or phrases based on the surrounding context.
More about Contextual Embeddings:
Contextual Embeddings are vector representations of words, phrases, or sentences that capture their meaning within a specific context. Unlike static embeddings, contextual embeddings adjust their representation based on the input sequence, making them ideal for tasks like semantic search and dense retrieval.
These embeddings are crucial for systems like retrieval-augmented generation (RAG), where understanding context improves the relevance and accuracy of results.
Frequently Asked Questions
How do contextual embeddings differ from static embeddings?
Contextual embeddings adjust their representation based on surrounding context, while static embeddings remain fixed.
What models are commonly used for generating contextual embeddings?
Models like BERT, RoBERTa, and GPT are widely used for generating contextual embeddings.
From the blog

Why Are Chatbots a Great Tool for Strategically Using Marketing Automation and AI?
Discover the synergy between chatbots, marketing automation, and AI. Learn how tools like SiteSpeakAI are revolutionizing the way businesses engage with customers and streamline marketing efforts.

Herman Schutte
Founder

Create a free GPT chatbot with SiteSpeakAI
Find out how you can easily create a fully customizable GPT-3 (or GPT-4) customer support chatbot for your business for free.

Herman Schutte
Founder