Learn about WorkFusion models

WorkFusion offers models for but not limited to the following use cases:

• Classification

  Classification stands for arrangement in groups or categories according to established criteria. Both binary and multi-class are supported.

  Examples include email classification, document identification, classification of pages within documents, approval process.

• Information extraction

  Information extraction involves extracting structured information, or key facts, from unstructured or semi-structured documents.

  Examples are extraction of details from invoices, claims, and financial documents.

Hyper model

There are two types of hyper models:

• A fixed set model containing an optimal model configuration created by a Data Scientist for a specific use case.

• A Search Engine model that finds the optimal configuration by running a Search Engine. Typically requires more time and resources compared to a fixed set model.

Each hyper model has a number of parameters defining prerequisites. For example, minDocs stands for the minimum amount of documents required for training,deltaDocs is the number of documents that can trigger automatic re-training.

Single model vs. multi-model

Under the hood, WorkFusion allows two types of models 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 use case. Fields are 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 use case.

This approach is optimized for a higher automation rate and accuracy.