In the vector search setup, we normally combine a fast embedding model and an accurate but slow reranker model. 
text
In the vector search setup, we normally combine a fast embedding model and an accurate but slow reranker model. 
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new_line
The newly released 
text
The newly released 
@jinaai
mention
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. 
text
 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. 
new_line
new_line
These models are available on Huggingface and has been integrated into the latest SentenceTransformers 2.7.0. Check it out!
text
These models are available on Huggingface and has been integrated into the latest SentenceTransformers 2.7.0. Check it out!
new_line
new_line
https://huggingface.co/jinaai/jina-reranker-v1-turbo-en
jinaai/jina-reranker-v1-turbo-en
model
resource
https://huggingface.co/jinaai/jina-reranker-v1-turbo-en
new_line
https://huggingface.co/jinaai/jina-reranker-v1-tiny-en
jinaai/jina-reranker-v1-tiny-en
model
resource
https://huggingface.co/jinaai/jina-reranker-v1-tiny-en
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
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Tom Aarsen
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