Learn about WorkFusion models
AutoML SDK models (also, AutoML models) are out-of-the-box Work.AI models comprising a combination of AutoML SDK components, such as algorithms and their parameters, annotators, feature extractors, pre- and post-processors.
Compatibility: All Work.AI versions
Hardware requirements: 8 CPUs, 32 GB RAM, disk storage 150 GB for training and inference
Associated Work.AI components:
Use cases
AutoML models are used for, but not limited to, the following major use cases:
Classification means arranging objects in groups or categories based on established criteria. Both binary and multi-class grouping is supported. Examples include email classification, document identification, classification of pages within documents, and approval process.
Information extraction (IE) involves extracting structured information, or key facts, from unstructured or semi-structured documents. Examples are extraction of details from invoices, claims, and financial documents.
The following models are intended to support the classification and IE use cases in the following way:
Checker models improve information extraction results. The model is trained to identify incorrect values produced by the original model and to post-process them, that is, validate and remove, if needed. For details, read the Checker model topic.
Grouping models enable developers to combine existing or new models into a single artifact to reduce the number of Workers and minimize the start time on the cluster. For details, read the Grouping models topic.
Model pipelines
There are two model pipeline (previously known as hyper model) types:
A fixed set containing an optimal model configuration created by a data scientist for a specific use case.
A Search Engine-based one assuming that an optimal configuration is found by running a Search Engine. Typically, the type requires more time and resources than a fixed set.
Single-model vs multi-model approach
Under the hood, WorkFusion allows using one of the two approaches for handling complex documents:
- Single-model
- Multi-model
Single-model approach
This approach implies training a single binary model for all fields. It works when you need to extract fields with the same semantic meaning. All fields are equally important and used in a specific use case. These fields are also subject to post-processing.
This approach is optimized for training and execution.
Multi-model approach
This approach implies training a set of individual binary models per field. It works when the semantic meaning of fields can vary, which means different subsets of fields are used in a specific use case.
This approach is optimized for a higher automation rate and accuracy.
For troubleshooting tips, refer to the following support guides: