Sold by: Jina AI
Deployed on AWS
A fast neural text reranking model supporting 8192 sequence length
Overview
Jina Reranker v1 Tiny model is a neural text reranking model, designed to enhance the relevance of search results. This model is the fastest reranker model in the Jina Reranker suite of models, offering fast and memory-efficient reranking process.
For our most accurate (and larger) reranker models, please see Jina Reranker v1 Base - en or Jina Reranker v1 Turbo - en.
Jina Reranker v1 Tiny 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 efficiency: While performing slighly lower than Jina Reranker v1 Base - en on the benchmarks, this model can process (rerank) five 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 competitively 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 hour for each host running the reranker model. Pricing is organized by AWS instance type and inference mode. Batch mode covers the ml.p2, ml.p3, and ml.g4dn families for scheduled, bulk workloads. Real-time mode covers those same families plus the ml.g5 family for live requests. Within each family, larger instances carry more compute and a different hourly rate, so you scale by choosing instance size. There is no upfront commitment; charges accrue per host hour based on the instance and mode you run.
Top-of-mind questions for buyers
You pay for the reranker model running on one AWS SageMaker instance of the chosen type. Billing meters wall-clock time that the host stays running, regardless of how many documents you rerank. Each active host accrues charges by the hour based on its instance type and inference mode.
Both meter running host hours, but they suit different workloads. Batch mode fits scheduled, bulk reranking jobs you run and then stop. Real-time mode keeps a host running to serve live requests. Real-time mode also supports the ml.g5 family, which batch mode does not.
Charges accrue per running host hour, not per rerank request. An idle but running host still bills at its hourly rate. To stop software charges, you stop the host. Stopped instances may still incur underlying AWS storage fees separate from the model charge.
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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 Tiny 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 return. | Default value: Same as the number of documents in the input
Type: Integer | No |
top_n | Top n documents to return. | Default value: Same as the number of documents in the input
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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