Sold by: MphasisÂ
Deployed on AWS
This algorithm trains a supervised text classification model and uses Active Learning to provide the most relevant samples for tagging
Overview
Active Learning for Text Classification trains a text classification model using a small corpus of training data and provides the most appropriate samples from a huge corpus of unlabeled data to be annotated in order to improve the model accuracy significantly. Using Active Learning this algorithm helps in identifying the most effective data sample to be tagged first thus reducing the time and effort to build a usable Machine learning model.
Highlights
- Active Learning for Text Classification can be used for prioritizing the data labeling task and thereby drastically reduce the data tagging effort required to build a working Machine Learning model.
- This solution can be used to iteratively sample the right data points & train the Machine Learning model to build a supervised machine learning algorithm. It helps in identifying which samples to label first based on the rules learned by Machine Learning model. It uses Active Learning methodologies to select the right sample data points from unlabeled data to build a better performing machine learning model much faster.
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Details
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Delivery method
Latest version
Deployed on AWS
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Features and programs
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AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
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 | $20.00 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large instance type, real-time mode | $10.00 |
ml.m5.4xlarge Training Recommended | Algorithm training on the ml.m5.4xlarge instance type | $10.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $20.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $20.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $20.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $20.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $20.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $20.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $20.00 |
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Delivery details
Amazon SageMaker algorithm
An Amazon SageMaker algorithm is a machine learning model that requires your training data to make predictions. Use the included training algorithm to generate your unique model artifact. Then deploy the model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
This is the Version 1.4 of the algorithm
Additional details
Inputs
- Summary
If you are using real time inferencing, please create the endpoint first and then use the following command to invoke it:
aws sagemaker-runtime invoke-endpoint --endpoint-name "endpoint-name" --body fileb://$file_name --content-type text/csv --accept application/output.csv
- Input MIME type
- application/zip, text/csv, text/plain, application/json
https://github.com/Mphasis-ML-Marketplace/Active-Learning-for-Text-Classification/tree/main/Test%20Input
https://github.com/Mphasis-ML-Marketplace/Active-Learning-for-Text-Classification/tree/main/Test%20Input
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
ID | Unique identifier associated with text | Type: Integer | Yes |
Text | Textual data | Type: FreeText | Yes |
Resources
Vendor resources
Support
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