Sold by: Jina AI
Deployed on AWS
A state-of-the-art neural text reranking model supporting 8192 sequence length
Overview
Jina Reranker v1 Base 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, improved information retrieval, and better data organization.
Use-cases: Vector search, retrieval augmented generation.
See our embedding models (Jina Embeddings v2) on Sagemaker for state-of-the-art 8k embedding models for vector search.
Highlights
- Extended context length: This reranker model is capable of handling queries up to 512 tokens and documents as large as 8192 tokens.
- High performance across the board: This reranking model ranks at the top compared to its competitors, in terms of 'Mean Reciprocal Rank' (MRR), according to BIER, MTEB, LoCo and an independent benchmark by LlamaIndex. A higher MRR represents a higher chance that the most relevant document to a query is returned with the highest relevance score by a reranking model.
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Latest version
Deployed on AWS
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.g4dn.2xlarge Inference (Batch) Recommended | Model inference on the ml.g4dn.2xlarge instance type, batch mode | $2.20 |
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 |
Dimensions summary
You pay by the hour for the GPU instance that runs the reranking model, billed per host hour. Two inference modes set the structure: batch mode processes large jobs at once, while real-time mode serves live requests. Batch mode is offered on p2, p3, and g4dn instance families. Real-time mode adds the g5 family alongside those options. Within each family, larger instance sizes carry higher hourly rates because they provide more GPU capacity. Choose the mode that matches your workload, then pick the instance size that fits your throughput needs. You control cost by scaling instances up or down.
Top-of-mind questions for buyers
You pay for one running GPU instance for one hour of wall-clock time. The instance runs the reranking model and processes your requests. The rate reflects the GPU capacity of the instance size you select, so larger sizes cost more per hour.
Charges accrue per host hour while the instance runs, whether or not it processes requests. A stopped instance stops software host-hour charges. Idle-but-running instances still bill. Underlying AWS storage or infrastructure fees may apply separately from the software charge.
Both meter per host hour. Batch mode runs the instance to process a large job, then you shut it down, so you pay only for the processing window. Real-time mode keeps the instance running to serve live requests continuously, billing every hour it stays up.
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Delivery details
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.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
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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
https://github.com/jina-ai/jina-sagemaker/blob/main/examples/sample-reranking-inference-input.json
https://github.com/jina-ai/jina-sagemaker/blob/main/examples/sample-reranking-batch-input.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: Number of top-ranked documents to be returned
Type: Integer | No |
top_n | Top n documents to rerank. | Default value: Number of top-ranked documents to be returned
Type: Integer | No |
Resources
Vendor resources
Support
Vendor support
We provide support for this model package through our enterprise support channel.
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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