Sold by: Jina AIÂ
Deployed on AWS
ColBERT multi-vector embedding model for multilingual text input of size up to 8192 tokens
Overview
jina-colbert-v2 is an open-source multilingual ColBERT-style embedding model supporting 8192 sequence length.
This model produces vectors of size 64 for tokens in input documents.
ColBERT (Contextualized Late Interaction over BERT) leverages the deep language understanding of BERT while introducing a novel interaction mechanism. This mechanism, known as late interaction, allows for efficient and precise retrieval by processing queries and documents separately until the final stages of the retrieval process.
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
- Trained from scratch with support for 89 major world languages.
- Matryoshka embeddings, which allow users to trade between efficiency and precision flexibly.
- Ability to process significantly longer contexts (up to 8192 tokens) compared to the original ColBERT. This capability is crucial for handling documents with extensive content, providing more detailed and contextual search results.
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.g5.8xlarge Inference (Real-Time) | Model inference on the ml.g5.8xlarge instance type, real-time mode | $18.25 |
ml.g5.12xlarge Inference (Real-Time) | Model inference on the ml.g5.12xlarge instance type, real-time mode | $27.50 |
ml.g5.2xlarge Inference (Real-Time) | Model inference on the ml.g5.2xlarge instance type, real-time mode | $4.75 |
ml.g5.4xlarge Inference (Real-Time) | Model inference on the ml.g5.4xlarge instance type, real-time mode | $9.30 |
ml.g5.48xlarge Inference (Real-Time) | Model inference on the ml.g5.48xlarge instance type, real-time mode | $90.00 |
ml.g5.16xlarge Inference (Real-Time) | Model inference on the ml.g5.16xlarge instance type, real-time mode | $35.00 |
ml.g5.24xlarge Inference (Real-Time) | Model inference on the ml.g5.24xlarge instance type, real-time mode | $52.00 |
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)Â .
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
ColBERT multi-embeddings model V2 - 64 dimensions
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?" } ], "parameters": { "input_type": "document" } }
- Input MIME type
- text/csv
https://github.com/jina-ai/jina-sagemaker/blob/main/examples/sample-colbert-inference-input.json
https://github.com/jina-ai/jina-sagemaker/blob/main/examples/sample-colbert-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 |
parameters | Type of input to get embedding, should be either 'document' or 'query'. | Default value: document
Type: FreeText
Limitations: Should be either 'document' or 'query'. | No |
input_type | Type of input to get embedding, should be either 'document' or 'query'. | Default value: document
Type: FreeText
Limitations: Should be either 'document' or 'query'. | No |
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
Jina ColBERT v2 Reranker model for Multilingual Documents
New State-of-the-Art Multilingual Embeddings With Task LoRA
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.