Amazon Sagemaker
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.
Keyword based Labeling for Text Data
By:
Latest Version:
3.1
This solution generates enhanced class labels for user provided unlabeled text data.
Product Overview
This solution helps create large training datasets without manually labeling them over weeks or months. It uses weak supervision approach and keyword based heuristics with the help of labeling functions (LFs) to assign labels to unlabeled training data. The labels are further enhanced using confidence learning methodologies to provide clean labeled datat as output. The output contains a CSV file consisting of the text, keyword based labels and enhanced clean labels. The solution is beneficial for obtaining automated clean class labels for input text datasets with less manual effort.
Key Data
Version
By
Type
Algorithm
Highlights
This solution leverages data-centric approach to get better class labels. This is extremely pertinent for downstream supervised model building. One can use this solution in domains such as e-commerce, marketing and fintech companies to automate the labeling of unlabelled text classification problems such as sentiment classification for product reviews, tweets or social media posts, finance news etc.
The current solution only works with dataframes as input and generates output that contains only those data points that are labeled by the labeling function. It does not include any data points that have not been assigned any base label. For better results, we recommend upto 700 words in each row.
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Pricing Information
Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.
Contact us to request contract pricing for this product.
Estimating your costs
Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.
Version
Region
Software Pricing
Algorithm Training$10/hr
running on ml.m5.xlarge
Model Realtime Inference$0.00/hr
running on ml.m5.xlarge
Model Batch Transform$0.00/hr
running on ml.m5.xlarge
Infrastructure PricingWith Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
SageMaker Algorithm Training$0.23/host/hr
running on ml.m5.xlarge
SageMaker Realtime Inference$0.23/host/hr
running on ml.m5.xlarge
SageMaker Batch Transform$0.23/host/hr
running on ml.m5.xlarge
Algorithm Training
For algorithm training in Amazon SageMaker, the software is priced based on hourly pricing that can vary by instance type. Additional infrastructure cost, taxes or fees may apply.InstanceType | Algorithm/hr | |
|---|---|---|
ml.m4.4xlarge | $10.00 | |
ml.c5n.18xlarge | $10.00 | |
ml.g4dn.4xlarge | $10.00 | |
ml.m5.4xlarge | $10.00 | |
ml.m4.16xlarge | $10.00 | |
ml.m5.2xlarge | $10.00 | |
ml.p3.16xlarge | $10.00 | |
ml.g4dn.2xlarge | $10.00 | |
ml.c5n.xlarge | $10.00 | |
ml.m4.2xlarge | $10.00 | |
ml.c5.2xlarge | $10.00 | |
ml.p3.2xlarge | $10.00 | |
ml.c4.2xlarge | $10.00 | |
ml.g4dn.12xlarge | $10.00 | |
ml.m4.10xlarge | $10.00 | |
ml.c4.xlarge | $10.00 | |
ml.m5.24xlarge | $10.00 | |
ml.c5.xlarge | $10.00 | |
ml.g4dn.xlarge | $10.00 | |
ml.p2.xlarge | $10.00 | |
ml.m5.12xlarge | $10.00 | |
ml.g4dn.16xlarge | $10.00 | |
ml.p2.16xlarge | $10.00 | |
ml.c4.4xlarge | $10.00 | |
ml.m5.xlarge Vendor Recommended | $10.00 | |
ml.c5.9xlarge | $10.00 | |
ml.m4.xlarge | $10.00 | |
ml.c5.4xlarge | $10.00 | |
ml.p3.8xlarge | $10.00 | |
ml.m5.large | $10.00 | |
ml.c4.8xlarge | $10.00 | |
ml.c5n.2xlarge | $10.00 | |
ml.p2.8xlarge | $10.00 | |
ml.g4dn.8xlarge | $10.00 | |
ml.c5n.9xlarge | $10.00 | |
ml.c5.18xlarge | $10.00 | |
ml.c5n.4xlarge | $10.00 |
Usage Information
Training
The user needs two files:
- dataset.csv containing the input text data.
- pattern.json containing details of column name in which data labeling will be applied along with keyword and class names based on which data will be labeled automatically.
Channel specification
Fields marked with * are required
training
*Input modes: File
Content types: text/csv, application/zip, application/gzip
Compression types: None, Gzip
Model input and output details
Input
Summary
input_zip.zip contains input_zip folder. input_zip folder contains:
- dataset.csv: containing the data in which automatic data labeling will be applied.
- pattern.json: containing parameters:
- column name: name of the column in the dataset.csv in which data labeling algorithm will be applied.
- class: dictionary containing the class names among which data will be divided based on the keywords belonging to that particular class.
Limitations for input type
1. Input should be in zip format and name should be input_zip.zip.
2. input_zip.zip should contain a input_zip folder.
3. input_zip folder should contain 2 files. One is a csv file "dataset.csv" and another is a json file "pattern.json"
4. Current solution only works with dataframes as input.
Input MIME type
text/csv, application/zip, application/gzip, text/plainSample input data
Output
Summary
output.zip will contain two files:
- output.csv* contains the original input data appended with two new columns "base label" and "clean label". The base label is created using keyword based weak supervision approach and clean label is generated using confidence based learning technique to improvise on the weak labels.
- tagged_label.json containing the tagged classes.
- In this listing, there is no inferencing required.
Output MIME type
text/csv, application/gzip, application/zip, text/plainSample output data
Sample notebook
Additional Resources
End User License Agreement
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Support Information
Keyword based Labeling for Text Data
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