
Overview
Given an incomplete sentence, this model predicts sentences that are most likely to occur in the context.
For example, given input "I love it", it will generate samples like "I love it and enjoy one of their series, Schafer or Clive ."
Highlights
- This model generates text most likely to follow given inputs.
Details
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Features and programs
Financing for AWS Marketplace purchases
Pricing
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m4.xlarge Inference (Batch) Recommended | Model inference on the ml.m4.xlarge instance type, batch mode | $0.00 |
ml.m4.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m4.xlarge instance type, real-time mode | $0.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $0.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $0.00 |
ml.m5.12xlarge Inference (Batch) | Model inference on the ml.m5.12xlarge instance type, batch mode | $0.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $0.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $0.00 |
ml.c4.4xlarge Inference (Batch) | Model inference on the ml.c4.4xlarge instance type, batch mode | $0.00 |
ml.m5.xlarge Inference (Batch) | Model inference on the ml.m5.xlarge instance type, batch mode | $0.00 |
ml.c5.9xlarge Inference (Batch) | Model inference on the ml.c5.9xlarge instance type, batch mode | $0.00 |
Vendor refund policy
None.
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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.
Version release notes
Initial release
Additional details
Inputs
- Summary
Supports JSON input.
AWS APIs can be used to invoke the model after endpoint creation, for example, using aws-cli:
aws sagemaker-runtime invoke-endpoint --endpoint-name your_endpoint_name --body '{"data":"I love it", "k": 5}' --content-type application/json --accept application/json >(cat) 1>/dev/null
"data" is required field, specifying the input sequence. "k" is an optional field, speficying how many samples to be generated. "k" must be less than or equal to 5.
- Input MIME type
- application/json
Resources
Vendor resources
Support
Vendor support
For model usage support, please refer to GluonNLP. Searching for related questions or open new issues.
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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