Sold by: Widn.AIÂ
Deployed on AWS
COMETKiwi provides state-of-the-art quality estimation for machine translations without requiring reference translations.
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/Â .
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.
Details
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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.g5.2xlarge Inference (Batch) Recommended | Model inference on the ml.g5.2xlarge instance type, batch mode | $20.00 |
ml.g5.2xlarge Inference (Real-Time) Recommended | Model inference on the ml.g5.2xlarge instance type, real-time mode | $20.00 |
ml.m5.xlarge Inference (Batch) | Model inference on the ml.m5.xlarge instance type, batch mode | $20.00 |
ml.m5.xlarge Inference (Real-Time) | Model inference on the ml.m5.xlarge instance type, real-time mode | $20.00 |
Vendor refund policy
No refunds. Please contact support+aws@widn.ai for further assistance.
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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
Fix issue with loading tokenizer.
Additional details
Inputs
- Summary
The model accepts JSON input containing batches of source-translation pairs with optional reference translations.
- Input MIME type
- application/json
https://github.com/widn-ai/aws-marketplace/blob/865885a2e66333008c76b7ca39d31c0fbfcb1b7a/widn-comet-wmt22-cometkiwi-da.ipynb
https://github.com/widn-ai/aws-marketplace/blob/865885a2e66333008c76b7ca39d31c0fbfcb1b7a/widn-comet-wmt22-cometkiwi-da.ipynb
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
data[].src | The source text to be evaluated. | - | Yes |
data[].mt | The machine-translated text to be evaluated. | - | Yes |
data[].ref | The reference translation (optional). | - | No |
Resources
Vendor resources
Support
Vendor support
Contact us at support+aws@widn.ai .
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