Sold by: IsaacusÂ
Deployed on AWS
A state-of-the-art legal reranking and zero-shot classification/NLI model.
Overview
Kanon Universal Classifier is a state-of-the-art legal reranking and zero-shot classification/NLI model optimized for scoring the relevance of queries to legal documents and classifying legal documents. With only 371 million parameters, it manages to outperform its largest open-source equivalent (DeBERTa v3 large) at legal classification while being 71% faster.
Kanon Universal Classifier supports a local context window of 512 tokens but can process documents of any length thanks to Isaacus' semchunk semantic chunking algorithm (https://github.com/isaacus-dev/semchunk ).
On a g6.xlarge instance, Kanon Universal Classifier can rerank or classify up to 131 million tokens per hour (equivalent to roughly 32k legal documents).
Like all other Isaacus SageMaker model deployments, your Kanon Universal Classifier deployment will be fully air-gapped--no data will enter or leave your AWS account.
Conveniently, Isaacus SageMaker models are also compatible with the standard Isaacus Python SDK via the Isaacus SageMaker Python integration (https://docs.isaacus.com/integrations/amazon-sagemaker ).
You can negotiate a discount to Kanon Universal Classifier by contacting us at https://isaacus.com/support .
Kanon Universal Classifier can also be purchased in a discounted bundle alongside Kanon 2 Embedder here: https://aws.amazon.com/marketplace/pp/prodview-lquokmsovgpsm .
Highlights
- Outperforms DeBERTa v3 at legal zero-shot classification while running 71% faster.
- Supports legal documents of any length thanks to Isaacus' [semchunk](https://github.com/isaacus-dev/semchunk) semantic chunking algorithm.
- Capable of processing ~32k legal documents (131 million tokens) per hour on a single `g6.xlarge` instance.
Details
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Delivery method
Latest version
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.g6.xlarge Inference (Batch) Recommended | Model inference on the ml.g6.xlarge instance type, batch mode | $4.99 |
ml.g6.xlarge Inference (Real-Time) Recommended | Model inference on the ml.g6.xlarge instance type, real-time mode | $4.99 |
ml.g5.xlarge Inference (Batch) | Model inference on the ml.g5.xlarge instance type, batch mode | $4.99 |
ml.g5.2xlarge Inference (Batch) | Model inference on the ml.g5.2xlarge instance type, batch mode | $4.99 |
ml.g5.4xlarge Inference (Batch) | Model inference on the ml.g5.4xlarge instance type, batch mode | $4.99 |
ml.g5.8xlarge Inference (Batch) | Model inference on the ml.g5.8xlarge instance type, batch mode | $4.99 |
ml.g5.16xlarge Inference (Batch) | Model inference on the ml.g5.16xlarge instance type, batch mode | $4.99 |
ml.g6.2xlarge Inference (Batch) | Model inference on the ml.g6.2xlarge instance type, batch mode | $4.99 |
ml.g6.4xlarge Inference (Batch) | Model inference on the ml.g6.4xlarge instance type, batch mode | $4.99 |
ml.g6.8xlarge Inference (Batch) | Model inference on the ml.g6.8xlarge instance type, batch mode | $4.99 |
Vendor refund policy
To the maximum extent permitted by law, there are no refunds for consumption of this product.
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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
Patched release of this model with version 0.1.3 of the Isaacus SageMaker Model Server, offering feature parity with version 0.7.0 (https://github.com/isaacus-dev/openapi/blob/8591b10de78a2b028df3f74fb5d6574d23bb62b2/openapi.yaml ) of the Isaacus API.
This patch disables support for older AWS instance types with outdated CUDA versions barring g5.2xlarge for batch transformation which, although not working, must be included due to AWS' own limitations preventing validation with newever instances.
Additional details
Inputs
- Summary
For a user-friendly walkthrough of how to get started deploying Isaacus models on SageMaker, check out the Isaacus SageMaker quickstart guide on our docs.
This model runs on the fully air-gapped Isaacus SageMaker Model Server, which supports all the same functionality as the standard Isaacus API except that requests to the server must be proxied through the /invocations endpoint.
For example, if you wanted to send a POST request to /v1/rerankings with the data {"model":"kanon-universal-classifier","query":"Who is the Governor-General?","texts":["The Governor-General is Sam Mostyn.","The King is Charles III."]}, you could so by sending /invocations the payload {"path":"/v1/rerankings","data":{"model":"kanon-universal-classifier","query":"Who is the Governor-General?","texts":["The Governor-General is Sam Mostyn.","The King is Charles III."]}}.
This means that minimal code changes are necessary to switch between the online Isaacus API and your own private Isaacus model deployments.
In fact, Python users can use the Isaacus SageMaker Python integration to automatically forward requests to the Isaacus API to SageMaker deployments using the standard Isaacus SDK.
Given that this is a private deployment and that authentication is managed by AWS, Isaacus API keys are not needed and are ignored.
As a reranking and zero-shot classification model, Kanon Universal Classifier currently only supports the /v1/rerankings and /v1/classifications/universal endpoints.
- Limitations for input type
- All the same limitations applicable to the Isaacus API except for the need for an API key.
- Input MIME type
- application/json
{
"path": "/v1/rerankings",
"data": {
"model": "kanon-universal-classifier",
"query": "Who is the Governor-General?",
"texts": [
"The Governor-General is Sam Mostyn.",
"The King is Charles III."
]
}
}
{
"path": "/v1/rerankings",
"data": {
"model": "kanon-universal-classifier",
"query": "Who is the Governor-General?",
"texts": [
"The Governor-General is Sam Mostyn.",
"The King is Charles III."
]
}
}
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
path | The path of the API endpoint being invoked (e.g., `/v1/rerankings` or `/v1/classifications/universal`). | One of `v1/embeddings`, `/v1/rerankings`, `/v1/extractions/qa`, and `/v1/classifications/universal`. | Yes |
method | The HTTP method used for the invocation (e.g., `POST`). Defaults to `POST`. | One of `POST`. | No |
headers | The HTTP headers to include in the invocation request. Defaults to `null`/`None`, in which case no additional headers are sent. | Must be a mapping of strings to strings. | No |
data | The data to be sent as the body of the invocation request. This can be any serializable object. Defaults to `null`/`None`, in which case no body is sent. | No |
Resources
Vendor resources
Support
Vendor support
To get in touch with our support team, you can reach out via the support form on our website: https://isaacus.com/support . We endeavor to respond within 24 hours.
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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