5/5 Semantic search provides more accurate results, understanding context and nuance. It's a leap forward in the realm of search systems. Created by Luis Serrano, it's transforming the way we search. Learn more on semantic search:
@cohere
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Semantic Search: How Vector Embeddings Capture Meaning
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4/5 Imagine this – the sentence "Hello, how are you?" & "Hi, what’s up?" will get similar vectors, while "Tomorrow is Friday" gets a different one. That's the power of semantic search!
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Semantic Search: Text Embeddings for Accurate AI Responses
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3/5 Enter semantic search! It uses text embeddings to turn words into vectors. It then uses similarity to find the most similar vector among responses to the query. The outcome? A more accurate, relevant response!
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Keyword Search Limitations in AI-Powered Systems
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2/5 Keyword search? It was all about finding responses with the most common words. But it's not foolproof. For example, ask "Where is the world cup?" and you might get "Where in the world is my cup of coffee?" Not helpful, right?
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Semantic Search with Embeddings: Beyond Keyword Matching
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1/5 We're excited to share a fantastic video on semantic search by our very own, @luis_likes_math
. You'll learn about using embeddings and similarity to build a semantic search model. It's an upgrade from traditional keyword search! https://
youtu.be/KfKpCj4tjZg -
When Not to Trust Language Models: Memory Limitations
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11/ When Not to Trust Language Models: Investigating Effectiveness and Limitations of Parametric and Non-Parametric Memories Authors: @alextmallen
, @AkariAsai
, @hllo_wrld
, Rajarshi Das, @HannaHajishirzi
, @DanielKhashabi
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Start Building with Cohere: Free AI Platform Access
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6/ Ready to start building? Sign up for a free Cohere account today. Happy coding! https://
dashboard.cohere.ai/welcome/regist
er
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Cohere Embedding Archives Community Initiative for AI Builders
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5/ Got questions or want to share something cool you built with this? Drop by the Embedding Archives: Wikipedia thread on the Cohere Discord . We can't wait to see what you create!
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Cohere Multilingual Embeddings Index Wikipedia Articles
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2/ What's inside? Using Cohere's Multilingual embedding model, we've embedded millions of Wikipedia articles in multiple languages. Each article is broken down into passages, with an embedding vector calculated for each passage.
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94 Million Embedded Passages Across 10 Languages
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4/ Languages included: English, German, French, Spanish, Italian, Japanese, Arabic, Chinese (Simplified), Korean, and Hindi. That's a total of 94 million embedded passages!