content.raw: In the vector search setup, we normally combine a fast embedding model and an accurate but slow reranker model. 
content.type: text
content.value: In the vector search setup, we normally combine a fast embedding model and an accurate but slow reranker model. 
content.type: new_line
content.type: new_line
content.raw: The newly released 
content.type: text
content.value: The newly released 
content.raw: @jinaai
content.type: mention
content.user: jinaai
content.raw:  rerankers are small in size and almost as accurate as our base reranker. This means given a time constraint, it can scoring more candidate documents from embedding models and have a better chance to feed LLM the correct context for RAG generation. 
content.type: text
content.value:  rerankers are small in size and almost as accurate as our base reranker. This means given a time constraint, it can scoring more candidate documents from embedding models and have a better chance to feed LLM the correct context for RAG generation. 
content.type: new_line
content.type: new_line
content.raw: These models are available on Huggingface and has been integrated into the latest SentenceTransformers 2.7.0. Check it out!
content.type: text
content.value: These models are available on Huggingface and has been integrated into the latest SentenceTransformers 2.7.0. Check it out!
content.type: new_line
content.type: new_line
content.raw: https://huggingface.co/jinaai/jina-reranker-v1-turbo-en
content.id: jinaai/jina-reranker-v1-turbo-en
content.type: model
content.type: resource
content.url: https://huggingface.co/jinaai/jina-reranker-v1-turbo-en
content.type: new_line
content.raw: https://huggingface.co/jinaai/jina-reranker-v1-tiny-en
content.id: jinaai/jina-reranker-v1-tiny-en
content.type: model
content.type: resource
content.url: https://huggingface.co/jinaai/jina-reranker-v1-tiny-en
rawContent: In the vector search setup, we normally combine a fast embedding model and an accurate but slow reranker model. 
  
  The newly released @jinaai rerankers are small in size and almost as accurate as our base reranker. This means given a time constraint, it can scoring more candidate documents from embedding models and have a better chance to feed LLM the correct context for RAG generation. 
  
  These models are available on Huggingface and has been integrated into the latest SentenceTransformers 2.7.0. Check it out!
  
  https://huggingface.co/jinaai/jina-reranker-v1-turbo-en
  https://huggingface.co/jinaai/jina-reranker-v1-tiny-en
reactions.reaction: 🚀
reactions.users: tomaarsen qubvel-hf clem sigridjineth dev7halo ceoofcapybaras
reactions.reaction: ❤️
reactions.users: dev7halo jaisanrobert
commentators._id: 6317233cc92fd6fee317e030
commentators.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6317233cc92fd6fee317e030/cJHSvvimr1kqgQfHOjO5n.png
commentators.fullname: Tom Aarsen
commentators.name: tomaarsen
commentators.type: user