
Overview
Jina Reranker v2 model is a neural text reranking model, designed to enhance the relevance of search results. It complements text embedding models and refines search results by prioritizing documents relevant to a query. This state-of-the-art reranker model enables a variety of applications that rely on precise search results across different languages, improved information retrieval, and high document throughput.
Use-cases: Vector search, retrieval augmented generation.
Highlights
- High multilingual performance across the board: This reranker model ranks at the top compared to its competitors, in terms of 'Mean Reciprocal Rank' (MRR), recall and NDCG, according to BEIR, MKQA and AirBench.
- Agentic RAG: This reranker model supports function-calling and text-to-SQL aware document reranking for agentic RAG.
- Ultra-fast: 15x more documents throughput than bge-reranker-v2-m3, and 6x more than jina-reranker-v1-base-en.
Details
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Pricing
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.g4dn.xlarge Inference (Batch) Recommended | Model inference on the ml.g4dn.xlarge instance type, batch mode | $1.50 |
ml.g5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.g5.xlarge instance type, real-time mode | $2.50 |
ml.p2.xlarge Inference (Batch) | Model inference on the ml.p2.xlarge instance type, batch mode | $2.30 |
ml.p3.8xlarge Inference (Batch) | Model inference on the ml.p3.8xlarge instance type, batch mode | $25.00 |
ml.g4dn.4xlarge Inference (Batch) | Model inference on the ml.g4dn.4xlarge instance type, batch mode | $4.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $7.00 |
ml.g4dn.16xlarge Inference (Batch) | Model inference on the ml.g4dn.16xlarge instance type, batch mode | $14.50 |
ml.p2.8xlarge Inference (Batch) | Model inference on the ml.p2.8xlarge instance type, batch mode | $18.00 |
ml.g4dn.8xlarge Inference (Batch) | Model inference on the ml.g4dn.8xlarge instance type, batch mode | $7.60 |
ml.g4dn.12xlarge Inference (Batch) | Model inference on the ml.g4dn.12xlarge instance type, batch mode | $11.25 |
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Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Version release notes
Reranker v2 Multilingual
Additional details
Inputs
- Summary
The model accepts JSON inputs. Texts must be passed in the following format.
{ "data": { "documents": [{"text": "the dog is in my house"}, {"text": "he likes dog"}, {"text": "hello world"}], "query": "where is the dog", "top_n": 2 } }
- Input MIME type
- text/csv
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
data | A unranked array of texts for reranking. | Type: FreeText | Yes |
documents | A unranked array of texts for reranking. | Type: FreeText | Yes |
data | The query used for reranking. | Type: FreeText | Yes |
query | The query used for reranking. | Type: FreeText | Yes |
data | Top n documents to rerank. | Default value: Same number of documents as input
Type: Integer | No |
top_n | Top n documents to rerank. | Default value: Same number of documents as input
Type: Integer | No |
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We provide support for this model package through our enterprise support channel.
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