Sold by: Jina AI
Deployed on AWS
Text embedding model (base) for input of size up to 8192 tokens
Overview
Jina Embeddings v2 Base model is optimized for highly accurate embeddings - For speed of inference and memory efficiency use the Small model.
jina-embeddings-v2-base-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: 137M 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.
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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 running instance, with no upfront commitment. Pricing splits into two deployment modes: batch inference for scheduled or bulk jobs, and real-time inference for live requests. Within each mode, you choose an AWS GPU instance type from the p2, p3, g4dn, and g5 families. Hourly cost scales with instance size, so larger instances with more compute cost more per hour. Batch mode covers p2, p3, and g4dn types. Real-time mode covers those plus the g5 family. You match the instance to your throughput and latency needs.
Top-of-mind questions for buyers
One HostHrs unit is one hour that a chosen instance runs the model. You pay per running hour of each active instance. Stopped instances do not accrue software charges. You match instance size to your workload and are billed only while it runs.
Both meter per running instance-hour. Batch mode suits scheduled or bulk embedding jobs you run and shut down. Real-time mode keeps an instance running to serve live requests continuously. Batch covers p2, p3, and g4dn types; real-time adds the g5 family for latency-sensitive traffic.
This is an English monolingual model built on a BERT architecture, supporting sequence lengths up to 8192 tokens. It has 137 million parameters and runs on a single GPU. Longer inputs enable long-document retrieval, semantic similarity, reranking, and RAG use cases.
huggingface.co
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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
Hourly pricing and token tracking
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 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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