Sold by: Jina AI
Deployed on AWS
A state-of-the-art fast neural text reranking model supporting 8192 sequence length
Overview
Jina Reranker v1 Turbo model is a neural text reranking model, designed to enhance the relevance of search results. This model is the best balance between accuracy and performance, offering fast and more memory-efficient reranking process.
For our most accurate (and larger) reranker model, please see Jina Reranker v1 Base - en.
Jina Reranker v1 Turbo 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
- Trained for speed and accuracy: While performing only slighly worse than Jina Reranker v1 Base - en on some of the benchmarks, this model can process three times as many documents in the same time.
- 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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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.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 |
Dimensions summary
You pay by the host hour for running this reranking model on AWS. Pricing splits into two inference modes: batch, for processing grouped documents at once, and real-time, for on-demand requests. Within each mode, you choose a GPU instance size across the p2, p3, g4dn, and g5 families. Larger instances carry more compute and a higher hourly rate. Batch mode covers p2, p3, and g4dn types; real-time mode adds the g5 family. Your cost scales with the instance you select and the hours it runs. No upfront commitment applies.
Top-of-mind questions for buyers
One HostHrs unit is one hour that a single model instance runs on the GPU instance type you select. You are billed for each hour the instance stays active, regardless of how many documents you rerank during that hour. Charges stop when the instance is not running.
Both meter by host hours, but they serve different workloads. Batch mode processes grouped documents in scheduled runs and covers p2, p3, and g4dn instances. Real-time mode handles on-demand requests and adds the g5 family. You pick the mode and instance size that fits your workload.
Charges accrue per host hour while the instance runs, even during idle periods with no requests. The software meter tracks running time, not document volume. To stop software charges, you must stop the instance. Underlying AWS infrastructure fees may still apply separately.
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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
Reranker turbo model
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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