Amazon Sagemaker
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.
COMETKiwi
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Latest Version:
v1.0.0
COMETKiwi provides state-of-the-art quality estimation for machine translations without requiring reference translations.
Product Overview
COMETKiwi is a reference-free quality estimation model based on Unbabel's powerful COMET framework, optimized for real-time translation quality assessment. It evaluates machine translations by comparing source and translated texts, providing reliable quality scores that correlate well with human judgment. This is a commercial-friendly offering of the model publicly available at https://huggingface.co/Unbabel/wmt22-cometkiwi-da/ .
Key Data
Version
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Type
Model Package
Highlights
COMETKiwi excels at reference-free quality estimation tasks:
- Real-time quality evaluation of translations
- Support for diverse language pairs
- Integration with translation workflows
COMETKiwi was trained on a diverse multilingual dataset comprising millions of high-quality human judgments across various domains. While it excels in many languages, performance may vary for low-resource languages or highly specialized technical content.
Widn COMETKiwi supports quality estimation across a wide range of languages including major languages like English, Chinese, Spanish, French, German, Japanese, Korean, and Arabic, as well as less common languages such as Welsh, Malagasy, and Kurdish. The model can evaluate translation quality between any language pair from its supported set of over 90 languages, making it versatile for global translation needs.
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Pricing Information
Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.
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Estimating your costs
Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.
Version
Region
Software Pricing
Model Realtime Inference$20.00/hr
running on ml.m5.xlarge
Model Batch Transform$20.00/hr
running on ml.m5.xlarge
Infrastructure PricingWith Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
SageMaker Realtime Inference$0.23/host/hr
running on ml.m5.xlarge
SageMaker Batch Transform$0.23/host/hr
running on ml.m5.xlarge
Model Realtime Inference
For model deployment as Real-time endpoint in Amazon SageMaker, the software is priced based on hourly pricing that can vary by instance type. Additional infrastructure cost, taxes or fees may apply.InstanceType | Realtime Inference/hr | |
|---|---|---|
ml.m5.xlarge Vendor Recommended | $20.00 |
Usage Information
Model input and output details
Input
Summary
The model accepts JSON input containing batches of source-translation pairs with optional reference translations.
Limitations for input type
The input must be properly formatted JSON with required fields. Each batch can contain multiple translation pairs for efficient processing.
Input MIME type
application/jsonSample input data
Output
Summary
The model outputs quality scores for each translation pair in the batch, with values between 0 and 1 indicating translation quality, where 1 represents a perfect translation.
Output MIME type
application/jsonSample output data
Sample notebook
Additional Resources
End User License Agreement
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Support Information
AWS Infrastructure
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Learn MoreRefund Policy
No refunds. Please contact support+aws@widn.ai for further assistance.
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