jina-reranker-v2-base-multilingual
Maintainer: jinaai
133
👨🏫
| 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-v2-base-multilingual model is a transformer-based text reranking model trained by Jina AI. It is a cross-encoder model that takes a query and a document pair as input and outputs a score indicating the relevance of the document to the query. The model is trained on a large dataset of query-document pairs and is capable of reranking documents in multiple languages with high accuracy. Compared to the previous jina-reranker-v1-base-en model, the Jina Reranker v2 has demonstrated competitiveness across a series of benchmarks targeting text retrieval, multilingual capability, function-calling-aware and text-to-SQL-aware reranking, and code retrieval tasks.
Model inputs and outputs
Inputs
- Query: The input query for which relevant documents need to be ranked
- Documents: A list of documents to be ranked by relevance to the input query
Outputs
- Relevance scores: A list of scores indicating the relevance of each document to the input query
Capabilities
The jina-reranker-v2-base-multilingual model is capable of handling long texts with a context length of up to 1024 tokens, enabling the processing of extensive inputs. It also utilizes a flash attention mechanism to improve the model's performance.
What can I use it for?
You can use the jina-reranker-v2-base-multilingual model for a variety of text retrieval and ranking tasks, such as improving the search experience in your applications, enhancing the performance of your information retrieval systems, or integrating it into your AI-powered decision support systems. The model's multilingual capability makes it a suitable choice for global or diverse user bases.
Things to try
To get started with the jina-reranker-v2-base-multilingual model, you can try using the Jina AI Reranker API. This provides a convenient way to leverage the model's capabilities without having to worry about the underlying implementation details. You can also explore integrating the model into your own applications or experimenting with fine-tuning the model on your specific data and 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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