Sold by: Jina AIÂ
Overview
jina-colbert-v1-en is an open-source English ColBERT-style embedding model supporting 8192 sequence length. 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
- Jina-colbert-v1-en's main advancement is its backbone, jina-bert-v2-base-en, which enables processing of significantly longer contexts (up to 8192 tokens) compared to the original ColBERT that uses bert-base-uncased. This capability is crucial for handling documents with extensive content, providing more detailed and contextual search results.
- jina-colbert-v1-en's has a superior performance, especially in scenarios requiring longer context lengths vs the original ColBERTv2. Note that jina-embeddings-v2-base-en uses more training data, whereas jina-colbert-v1-en only uses MSMARCO, which may justify the good performance of jina-embeddings-v2-base-en on some tasks.
- Use-cases: Fine-grained vector search, retrieval augmented generation, long document clustering, sentiment analysis.
Details
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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 |
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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
ColBERT multi-embeddings model
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
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