#embeddingmodels resultados da pesquisa

🧠 EMBEDDING MODELS are the unsung heroes of RAG. They convert text into the "meaning vectors" that VectorDBs store. Choosing a good one (like OpenAI, Cohere, or an open-source model) is critical for search accuracy. What model provides the BEST embeddings today? #Embeddings #RAG

gm! Embedding Study 1 and 2, 2024 by @hollyherndon and @matdryhurst

✅ Bookmark : [Very Important LLM System Design #20] Embeddings: How It Actually Works ( With Implementation Code File) open.substack.com/pub/naina0405/…

Our guide on picking an embedding model is good too btw github.com/petabridge/mem…

So, what are these embeddings? They are derived from Geospatial Foundation Models (GeoFMs). The researchers focused on two specifically: • Presto (low compute requirements) • AlphaEarth (already generated globally) These models compress complex sensor data into…

embeddings basically map complex info into vectors so machines can 'understand' context better-really opens up possibilities for retrieval and generation tasks.

ouch i never realized how limited mixed-objective training really is. stapling image embedding models to a VLM as embedding prefixes seems to have very real representational limits you'll discover very quickly.

a way to think of embeddings: mathematical representation of multimodal data which keeps the semantic meaning.

“Embeddings help AI systems to find relevant information based on meanings and not just exact words. How does this work? Once the chunking is complete, a pre-trained model converts each text chunk to a vector embedding.”

a while ago I built this small tool as demo of how embeddings work, for educational purposes type a word or phrase and see the vector representation (reduced from 1536 dimensions to 3) similar concepts -> similar vectors (cos(α)≈1) you can try it & fork it, link below 👇

you might expect to find that the 'optimal embedding dim' for a model is low *compared to* the embedding dim of the 'entire image, taken as a big-ass token of channels*height*width values'. but higher resolution images with lower pixel redundancy must demand higher embedding dim!

used embeddings for sentiment analysis on customer reviews last month, got 90% accuracy at 20x the speed of gpt-4 turbo. sometimes simple wins over fancy

did an experiment a bit back, and found that embedding based classification seemed consistently faster and cheaper than using the cheapest/fastest model (at the time) x.com/yoheinakajima/…

Do some latent reasoning on these embedding vectors. Use a JePA-kind of model to perform the prediction on the latent vectors But what training objective though ? RL would be the first coming in mind 2/2

🚀Our NeurIPS 2025 paper verified Embedding-based Continuous Memory works better for Processing multimodal information! 🧠 8 Embeddings per item make your VLM stronger Reasoner across 8 multimodal and multilingual tasks. 🔍 Plug-and-play, easy-to-use, just try it on your task!

AI Terminology #4: Embeddings Vector representations of text, images, or other data that capture semantic meaning. They let models compare concepts by measuring distance in high-dimensional space. x.com/LangbaseInc/st…

9. Embeddings Math representations of meaning. They power semantic search, recommendations, clustering. Understanding embeddings = understanding AI’s “language of logic.”

Check out this video "Embeddings and Vector DB" twitch.tv/jordineils/v/2…

Made a face recognition model for a hackathon and instead of CNN, I used embeddings and similarity which made the process much more accurate and efficient. Accomplished sa feel ho rha hai😋

UForm #multimodal #ai #embeddingmodels

🔧 Their method is called “vec2vec”. It’s like Google Translate, but for AI number codes. It takes AI1's secret code and turns it into AI2's format—even without knowing what the text was! #AItranslation #EmbeddingModels #TechSimplified

ICYMI @bo_wangbo from @JinaAI_ gave a presentation at #UnstructuredData Meetup in Berlin, "From CLIP to JinaCLIP: General Text-Image Representation Learning for Search and Multimodal RAG" 🤔 🔍 Read the recap: zilliz.com/blog/clip-to-j… #JinaAI #EmbeddingModels #Meetup

#LanguageModels (LLMs and Chat Models) for generating & processing language. #EmbeddingModels for converting text to vectors used in semantic analysis. LLMs are more suited for standalone text generation tasks. Chat models excel in interactive, context-sensitive application

Advanced Considerations in RAG Performance: hubs.la/Q02lK0Fj0 Don't forget to join us on March 20th at 9 am as we explore the concepts of chunking, #embeddingmodels, and #vectordatabases! 🤓✨

IBM Unveils Efficient Granite Embedding Models for High-Performance AI Retrieval #IBM #EmbeddingModels #AI #MachineLearning #GraniteEmbedding itinai.com/ibm-unveils-ef… Introduction to IBM’s New Embedding Models IBM is making waves in the AI community with the release of two new…

Google AI Launches Gemini Embedding: Next-Gen Multilingual Text Representation Model #EmbeddingModels #ArtificialIntelligence #MachineLearning #SemanticSimilarity #DataScience itinai.com/google-ai-laun…

#LanguageModels #EmbeddingModels #AIInnovation #NLP #TechTrends #MachineLearning #DataScience #DigitalTransformation #InnovativeTech #ArtificialIntelligence Vital Role of Embedding Models in Large Language Systems 🔗diskmfr.com/vital-role-of-…

✍️ New Post Voyage AI Tackles AI Hallucinations with Advanced RAG Tools hub.dakidarts.com/voyage-ai-tack… #AI #ArtificialIntelligence(AI) #embeddingmodels #Exclusive #Funding #GenerativeAI #RAG #startup

This Wednesday @svonava will join a panel discussion at the Future of Data and AI 💡 Don't miss the chance to gain insights from him and other industry experts as they chat about #embeddingmodels and #vectorsearch! Get your free ticket: hubs.la/Q01CwGv20

The Invisible Guardians of AI: Unsung but Vital Models medium.com/p/the-invisibl… #AIecosystem #EmbeddingModels #AnomalyDetection #TimeSeries #FederatedLearning #KnowledgeAugmentedAI