Sold by: MphasisÂ
Deployed on AWS
The solution generates new text data from existing data using data augmentation at sentence level.
Overview
Natural Language Sentence Generator helps ML practitioners to significantly increase the diversity of data available for training models, without actually collecting new data. The effectiveness of machine learning based classifiers largely depends on the size and quality of the training data. This solution helps to increase the size of the dataset and introduce variability in the dataset.
Highlights
- The solution can be leveraged to augment text data at sentence level to generate new data with similar context from input sample.
- The solution generates additional text data from limited textual datasets and thereby solve class imbalance problem.
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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.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $16.00 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large instance type, real-time mode | $8.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $16.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $16.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $16.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $16.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $16.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $16.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $16.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $16.00 |
Vendor refund policy
Currently we do not support refunds, but you can cancel your subscription to the service at any time.
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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
Bug Fixes and Performance Improvement
Additional details
Inputs
- Summary
Input:
Following are the mandatory inputs guidelines: • The input csv should have a column named as "sentence" which contains the sentences • Supported content types: text/csv.
Output:
Instructions for output interpretation: • Output will be the augmented sentences with 3 predictions. • Output content type: A “.csv” file with the instances. • Supported content types: 'text/csv'.
Invoking endpoint:
If you are using real time inferencing, please create the endpoint first and then use the following command to invoke it:: aws sagemaker-runtime invoke-endpoint --endpoint-name "endpoint-name" --body fileb://sample.csv --content-type text/csv --accept text/csv out.csv
Resources:
- Input MIME type
- text/csv, text/plain
See Input Summary
See Input Summary
Resources
Vendor resources
Support
Vendor support
For any assistance reach out to us at:
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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