Sold by: Jina AI
Overview
jina-embeddings-v3 is a multilingual multi-task text embedding model designed for a variety of NLP applications. Based on the Jina-XLM-RoBERTa architecture, this model supports Rotary Position Embeddings to handle long input sequences up to 8192 tokens. Additionally, it features 5 LoRA adapters to generate task-specific embeddings efficiently.
Highlights
- **Extended Sequence Length**: Supports up to 8192 tokens with [RoPE](https://arxiv.org/abs/2104.09864).
- **Task-Specific Embedding**: Customize embeddings through the task argument with the following options: * *retrieval.query*: Used for query embeddings in asymmetric retrieval tasks * *retrieval.passage*: Used for passage embeddings in asymmetric retrieval tasks * *separation*: Used for embeddings in clustering and re-ranking applications * *classification*: Used for embeddings in classification tasks * *text-matching*: Used for embeddings in tasks that quantify similarity between two texts, such as STS or symmetric retrieval tasks
- **[Matryoshka Embeddings](https://arxiv.org/abs/2205.13147)**: Supports flexible embedding sizes (32, 64, 128, 256, 512, 768, 1024), allowing for truncating embeddings to fit your application.
Details
Sold by
Delivery method
Latest version
Deployed on AWS
Unlock automation with AI agent solutions
Fast-track AI initiatives with agents, tools, and solutions from AWS Partners.
Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
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.g5.xlarge Inference (Batch) Recommended | Model inference on the ml.g5.xlarge instance type, batch mode | $2.50 |
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.g4dn.4xlarge Inference (Batch) | Model inference on the ml.g4dn.4xlarge instance type, batch mode | $4.00 |
ml.g4dn.16xlarge Inference (Batch) | Model inference on the ml.g4dn.16xlarge instance type, batch mode | $14.50 |
ml.p2.16xlarge Inference (Batch) | Model inference on the ml.p2.16xlarge instance type, batch mode | $35.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $48.25 |
ml.g4dn.2xlarge Inference (Batch) | Model inference on the ml.g4dn.2xlarge instance type, batch mode | $2.20 |
ml.p3.8xlarge Inference (Batch) | Model inference on the ml.p3.8xlarge instance type, batch mode | $25.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $7.00 |
Vendor refund policy
Refunds to be processed under the conditions specified in EULA.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
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
Bug fixes.
Additional details
Inputs
- Summary
The model accepts JSON inputs. Texts must be passed in the following format.
{ "data": [ { "text": "How is the weather today?" }, { "text": "What's the color of an orange?" } ], "parameters": { "task": "text-matching", "late_chunking": false, "dimensions": 1024 } }
- Input MIME type
- text/csv
https://github.com/jina-ai/jina-sagemaker/blob/main/examples/sample-inference-v3-input.json
https://github.com/jina-ai/jina-sagemaker/blob/main/examples/sample-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.text | An array of strings for the model to embed. | Type: FreeText | Yes |
parameters.task | Select the downstream task for which the embeddings will be used. The model will return the optimized embeddings for that task. None can also be specified, indicating that no specific task is required. | Default value: None
Type: FreeText
Limitations: Should be one of 'retrieval.query', 'retrieval.passage', 'separation', 'classification', 'text-matching' or None. | No |
parameters.late_chunking | Apply the late chunking technique to leverage the model's long-context capabilities for generating contextual chunk embeddings.
Reference: https://jina.ai/news/late-chunking-in-long-context-embedding-models
| Default value: false
Type: FreeText
Limitations: boolean | No |
parameters.dimensions | Output dimensions. Smaller dimensions are easier to store and retrieve, with minimal performance impact thanks to MRL. | Default value: 1024
Type: Integer
Minimum: 1
Maximum: 1024 | 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.
Similar products
State-of-the-art legal embedding and reranking models optimized for legal research, legal RAG, and legal document classification.
Jina ColBERT v2 Reranker model for Multilingual Documents
Embedding model for cross-modal and multimodal retrieval for text, images and complex document inputs.
Text embedding model (small) for input of size up to 8192 tokens
Text embedding model (base) for input of size up to 8192 tokens
Customer reviews
No customer reviews yet
Be the first to review this product . We've partnered with PeerSpot to gather customer feedback. You can share your experience by writing or recording a review, or scheduling a call with a PeerSpot analyst.