jina-reranker-v1-turbo-en
Maintainer: jinaai
44
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The jina-reranker-v1-turbo-en model is a fast and efficient text reranking model developed by Jina AI. It is based on the JinaBERT architecture, which supports longer input sequences of up to 8,192 tokens. This allows the model to process more context and deliver better performance compared to other reranking models.
To achieve blazing-fast inference speeds, the jina-reranker-v1-turbo-en model employs knowledge distillation. A larger, slower model (jina-reranker-v1-base-en) acts as a teacher, transferring its knowledge to a smaller, more efficient student model. This student retains most of the teacher's accuracy while running much faster.
Jina AI also provides two other reranker models in this family - the even smaller jina-reranker-v1-tiny-en for maximum speed, and the larger jina-reranker-v1-base-en for the best overall accuracy.
Model inputs and outputs
Inputs
- Query: The text to be used as the search query
- Documents: The list of documents to be reranked based on the query
Outputs
- Reranked documents: The list of documents, reordered by their relevance to the input query
- Relevance scores: A score for each document indicating its relevance to the query
Capabilities
The jina-reranker-v1-turbo-en model excels at quickly and accurately reranking large sets of documents based on a given query. Its ability to process long input sequences makes it suitable for use cases involving lengthy documents, such as long-form content or technical manuals.
What can I use it for?
The jina-reranker-v1-turbo-en model can be integrated into a variety of search and recommendation systems to improve their performance. Some potential use cases include:
- Enterprise search: Rerank search results to surface the most relevant documents for a user's query.
- Technical documentation search: Quickly find the most relevant sections of lengthy technical manuals or product specifications.
- Recommendation systems: Rerank a set of recommended items or content based on a user's preferences or context.
Things to try
One interesting aspect of the jina-reranker-v1-turbo-en model is its ability to process long input sequences. This can be particularly useful for tasks involving lengthy documents, where other models may struggle to capture the full context. Try experimenting with the model's performance on various document lengths and see how it compares to other reranking approaches.
Additionally, the knowledge distillation technique used to create the jina-reranker-v1-turbo-en model is an interesting way to balance speed and accuracy. You could explore how the performance of the different reranker models in the Jina AI family compares, and see how the tradeoffs between speed and accuracy play out in your specific use case.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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