Use Case Documentation
Text extraction and classification

Choose this use-case if you wish to develop a model that classifies named entities present in unstructured text into predefined categories such as person names, organizations, medical codes, etc. Named entity recognition may be useful in information extraction, content recommendation, biomedical informatics, or academic research. Given a dataset containing raw text data and labels for entities in the corresponding text data, you can generate a model that maps entity labels to new text data.

Dataset and Feature Group Requirements

This section specifies the Datasets / Feature Groups requirements to successfully train a Text extraction and classification model. Feature requirements include recommendations on additional datasets that might enhance model performance.

Training Models - Training Options and Metrics

This section describes all the available model training options that can be used to create a Text extraction and classification model. You can utilize the metric explanations to better understand how they measure the performance of the model you trained.

Prediction API

This section discusses the prediction API method so that you could properly generate predictions from the model you deployed.