TDM for Classification Models

• Updated on Jul 22, 2026
• Published on May 6, 2026
• 15 minute(s) read

Classification models are a crucial part of document processing as they help the system determine which layout should be used to process each page you upload. Training Data Management for Classification allows you to add, remove, and update training pages for Semi-structured Classification (also known as NLC) models to achieve more accurate classification results. In this way, TDM helps you maximize the performance of your Classification models.

TDM for Classification logic

TDM for Classification operates on a document level. However, the Training Data tab displays the number of uploaded documents and the required and recommended number of pages per layout.

TDM for Classification allows you to manage example documents that should be included in or excluded from your model’s training:

• Layouts eligible to train — These are the layouts that meet the minimum number of pages required for training. To ensure this requirement is met, upload documents that:

• have pages that match your layout and

  • are diverse, but still represent your layout.

• Excluded documents— TDM uses these as examples of documents that you expect to process but don't want to match. They serve as counter-examples of the documents that your model should not classify.

Access TDM for Classification

To access TDM for Classification, go to the Models section. The Classification tab appears by default.

• A table with all Classification models appears:

The Classification models table contains the following columns:

• Model shows the name of your Classification model.
• Compatible Releases indicate the number of releases the Classification model can predict.
• Status displays the model's current state (e.g., Needs Training or Live).
• Date Deployed shows the date the model was deployed.

Using TDM for Classification

The Send documents to Training Data Management setting for Identification and Classification models allows you to control whether submission data is used for model training. It is disabled by default and can be managed from the System Settings ( Administration > System Settings).

Overview tab

Projected Automation Chart

The Projected Automation chart displays the performance of the model that’s currently live. The chart displays how the target accuracy affects the automation. The lower the accuracy, the higher the automation, and vice versa.

Model History table

The Model History table provides a comprehensive overview of your model’s lifecycle. It displays the following columns:

• Name — The name of the last available model for this layout.
• Date Created— Date and time the model was created. Helps in tracking the model’s version history.
• Version — The specific version of the model that was trained on.
• Source — Indicates where the model was trained—either within the current instance or externally.
• Proj auto — Displays the predicted automation based on the Test Target Accuracy.
• Docs Trained — The total number of documents used for training the model.
• Last Deploy — The last date and hour the model was deployed.

Training Data tab

Training Data Summary card

The Training Data Summary card displays insights on the status of your training dataset.

• Training Data Status indicates your training data's health based on the number of pages uploaded for each layout:
  • Requirements Not Met — The minimum number of required pages uploaded for each layout is 10.
  • Not Optimized — Hyperscience recommends uploading at least 120 pages to build a robust classification model.
  • Ready To Train— This status will be displayed after you’ve reached the minimum required and recommended number of uploaded pages to start a model training.

Training Data Health card

The Training Data Health card displays a breakdown of your dataset. It shows all layouts included in the Classification model, as well as bars next to each layout indicating the number of uploaded pages.

Training Data table

The Training Data table displays all documents that can be used as training data for the model.

Selecting at least one training document allows you to use the Actions drop-down menu. This drop-down menu has the following buttons:

• Edit— change the layout and the usage rule of the selected training documents.
• Export—you can export the Classification model’s training data to a CSV file.
• Delete rows

Excluded Training Data table

The Excluded Training Data table displays the documents used as counter-examples for your Classification model.

Training a Classification Model

Upload your documents

To upload documents to TDM for Classification:

1. Click the Add Training Data button on the right-hand side of the Training Data Health card.
2. Choose Upload Files or Import Training Data from the dialog box.

Review your documents

Match each document to a specific layout. You will match a whole document to a specific layout.

Training a Classification Model

After you’ve reached the requirements and recommendations, you’ll see a message indicating that your model is ready to be trained for the first time in the Overview tab.

• You can run training from either tab by clicking the Run Training button on the upper-right corner of the page.