Gradient AI, Cohere, and JinaAI have recently launched their own text embedding models to improve the quality and relevance of text generation. Gradient AI's embeddings product allows users to generate vector embeddings from text and implement RAG to improve LLM and reduce hallucination. Cohere released Embed v3, a text embedding model that delivers state-of-the-art performance on benchmarks and offers robustness to noisy datasets. JinaAI's text embedding model, jina-embeddings-v2, provides an 8,192-token context window and matches OpenAI's ada-002 on popular benchmarks. These embedding models aim to enhance the capabilities of language models and solve real-world problems.
new state of the art embeddings model just dropped! awesome work by @Nils_Reimers and the @cohere team :) Cohere is focused on making LLMs live up to hype and solve real world problems; embedding models are a core part of that! https://t.co/7OdJJwk3Ad
We just released Embed v3, our latest and most advanced text embedding model. Embed v3 delivers: - SOTA performance on trusted benchmarks like MTEB and BEIR - Robustness to noisy datasets - Compressed embeddings to save on storage costs Read more here: https://t.co/Bjp3XuP72d
There's a new text embedding model by @JinaAI_ with some exciting properties 👀 - 8,192-token context window (embed chapters, not pages) - Matches OpenAI's ada-002 on popular benchmarks Use jina-embeddings-v2 for search & recommendations and pair w/ LLMs like Mistral for RAG.
🤖 From this week's issue: An article that explains how to implement an advanced RAG pipeline using embeddings, cache, hybrid search, and ensemble retriever to improve the quality and relevance of text generation. https://t.co/CShtWp8myS
Did you know we launched our own embeddings product to let users generate vector embeddings from text? Check out how you can use our embeddings API and @llama_index to implement RAG to improve your LLM and reduce hallucination. https://t.co/TLmTSwRXBm https://t.co/yZaSEEmaFP
Did you know we launched our own embeddings product to let users generate vector embeddings from text? Check out how you can use our embeddings API and @llama_index to implement RAG to improve your LLM and reduce hallucination. https://t.co/UBIAdTGUuB https://t.co/z8O5Bm7dnH