Sold by: Jina AI
Deployed on AWS
Free Trial
Text embedding model (small) for input of size up to 8192 tokens
Overview
Jina Embeddings v2 Small model is optimized for speed of inference and memory efficiency - For higher accuracy, use the Base model.
jina-embeddings-v2-small-en is an open-source English embedding model supporting 8192 sequence length. This state-of-the-art AI embedding model enables many applications, such as document clustering, classification, content personalization, vector search, or retrieval augmented generation.
Highlights
- Use-cases: Vector search, retrieval augmented generation, long document clustering, sentiment analysis. Extended context length: This model uniquely support an 8K context length, enabling them to process and understand larger chunks of data in a single pass, resulting in richer embeddings and more accurate predictions. Model size: 32.7M parameters. High performance over tasks across the board: Our model ranks amongst the top performing ones on HuggingFace’s MTEB leaderboard for embedding models - especially considering its small size and extended context length.
- The backbone of this model was pretrained on the C4 dataset. This model is further trained on Jina AI's collection of more than 400 millions of sentence pairs and hard negatives. These pairs were obtained from various domains and were carefully selected through a thorough cleaning process.
Details
Sold by
Delivery method
Latest version
Deployed on AWS
New
Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
Features and programs
Trust Center
Access real-time vendor security and compliance information through their Trust Center powered by Drata or Vanta. Review certifications and security standards before purchase.
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
Free trial
Try this product free for 7 days according to the free trial terms set by the vendor.
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 running instance, with no upfront commitment. Pricing splits into two inference modes: batch mode for processing groups of inputs at once, and real-time mode for on-demand requests. Within each mode, you choose from GPU-backed AWS instance types across three families. Rates rise as instance size grows within a family, since larger instances add compute and memory. Batch mode covers a subset of these instance types, while real-time mode adds a further GPU family. Your total cost depends on which instance you run, which mode you pick, and how many hours it stays active.
Top-of-mind questions for buyers
The rate depends on the AWS GPU instance type you run and the inference mode. Larger instances within a family carry higher hourly rates because they add compute and memory. You pick the mode and instance, then pay per host-hour the instance stays active.
Software charges meter running host-hours only. A fully stopped instance stops accruing software charges. You are billed for the hours the instance stays active, regardless of how many inputs you process during that time. Underlying AWS infrastructure fees may still apply for stored resources.
No. On Marketplace deployment you pay per host-hour for the running instance, not per token or request. Your cost tracks how long the instance runs, not the number of texts embedded. Higher throughput within one hour does not raise the hourly charge.
huggingface.co
Helpful?
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
Monetization and tokens counting
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 is the weather like today?"}, {"text": "What's the color of an orange?"} ] }
- Input MIME type
- text/csv
https://github.com/jina-ai/jina-sagemaker/blob/main/examples/sample-inference-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 | An array of strings for the model to embed.
| Type: FreeText | Yes |
text | An array of strings for the model to embed.
| Type: FreeText | Yes |
Resources
Vendor resources
Support
Vendor support
We offer support through Jina AI enterprise support team.
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
Text embedding model (base) for input of size up to 8192 tokens
Jina ColBERT v2 Reranker model for Multilingual Documents
Embedding model for cross-modal and multimodal retrieval for text and image data
New State-of-the-Art Multilingual Embeddings With Task LoRA
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.