Sold by: Widn.AIÂ
Deployed on AWS
xCOMET provides explainable quality estimation for machine translations, offering detailed error detection alongside quality scores.
Overview
xCOMET is an explainable quality estimation model based on Unbabel's powerful COMET framework, optimized for transparent translation quality assessment. This is a commercial-friendly offering of the model publicly available at https://huggingface.co/Unbabel/XCOMET-XLÂ . Beyond providing quality scores, it identifies specific errors in translations by detecting error spans. This ~3.5B parameter model evaluates machine translations by comparing source and translated texts, and optionally reference translations, providing detailed feedback that aligns with the MQM (Multidimensional Quality Metrics) framework. This service deployment provides a commercial-friendly way to utilize the model's capabilities.
Highlights
- **xCOMET** excels at explainable quality estimation tasks: * Reference-free and reference-based translation evaluation (QE and MT evaluation) * Detailed error detection through error spans * Real-time quality evaluation of translations * Support for diverse language pairs * Integration with translation workflows
- **xCOMET** was trained on a diverse multilingual dataset comprising millions of high-quality human judgments across various domains. The model provides scores between 0 and 1 (where higher is better) along with error spans that help identify and understand specific translation errors. While it excels in many languages, performance may vary for low-resource languages or highly specialized technical content.
- **xCOMET** 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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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.g5.2xlarge Inference (Batch) Recommended | Model inference on the ml.g5.2xlarge instance type, batch mode | $40.00 |
ml.g5.2xlarge Inference (Real-Time) Recommended | Model inference on the ml.g5.2xlarge instance type, real-time mode | $40.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $40.00 |
ml.m5.2xlarge Inference (Real-Time) | Model inference on the ml.m5.2xlarge instance type, real-time mode | $40.00 |
ml.m5.xlarge Inference (Real-Time) | Model inference on the ml.m5.xlarge instance type, real-time mode | $40.00 |
Vendor refund policy
No refunds. Please contact support+aws@widn.ai for further assistance.
Legal
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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. See the notebook for examples of inputs.
- 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/json
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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 objects, each containing source (`src`), translation (`mt`), and optionally reference (`ref`) text. | Type: FreeText | Yes |
batch_size | Number of translations to process in one batch. | Default value: 8
Type: Integer
Minimum: 1
Maximum: 64 | No |
Resources
Vendor resources
Support
Vendor support
Contact us at support+aws@widn.aiÂ
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