TDM for Identification Models

• Updated on May 16, 2025
• Published on Oct 10, 2024

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In this article, you’ll learn how to navigate through and use Training Data Management for Identification models. Learn more about each feature in Training Data Management.

Accessing TDM for Identification models

Each identification model trained for the specific Semi-structured layout has its tab on the Model Management page (i.e., Field Identification or Table Identification). Find the type of model you want to manage by clicking on its respective tab.

You can upload or download training documents by clicking the buttons in the page's upper-right corner.

Navigating TDM for Identification models

Model Summary card

The Model summary card displays this layout's Live and Candidate models. You can see the following information:

• Status— the current status of the model:

• Live — The model is deployed.

  • Trained — You have a candidate model ready to be deployed.
  • Uploaded — The model was uploaded.
  • Failed — The model training has failed.
  • Training — The model training is still in progress.
  • Requirements not met — The requirements for training a model are not met.
  • Analysis running — Training Data Analysis is in progress.
  • Queued — The model is scheduled for training in the queue.

• Projected Automation— displays the predicted automation, based on the Test Target Accuracy.

• Projected Cell Automation— displays the projected automation per cell, based on the Test Target Accuracy.

• Test Target Accuracy— the accuracy percentage used to calculate the projected automation. You can adjust it from the Model History card.

• Documents Trained— the number of documents used for training the model.

• Fields Layout / Trained or Columns Table / Trained— The number of fields or columns in the current live version of your layout, and the number of fields or columns used for training the model.

• Trained— date the model was trained.

• Deployed— date the model was deployed.

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.

Note that projected model performance (i.e., accuracy and automation) can increase by adding more QA documents. You can also see the margin of error (MoE) for this model.

The Margin of Error (MoE) indicates the allowable range of inaccuracy in the system's results. It shows you how much the output can differ from the true value while still being acceptable. A smaller margin of error means the system is more accurate.

Identification Report

The Identification Report displays the number of identified fields (whether the machine or a human identified them), their accuracy, and the field-level automation (i.e., the automation of the fields the model was trained on).

Select a specific date range for the report to see charts for the total number of identified fields (machine-identified and human-identified) and their respective accuracy values.

Fields Identified Chart

The Fields Identified chart displays the number of machine- and manually-identified entries for a specific period.

• You can filter the Identification Entries by clicking the Total Fields, Manual, and Machine buttons.

Training Data Health Card

The Training Data Health card displays a breakdown of your dataset. It shows the following insights on the uploaded documents:

• The number of additional documents recommended for training.
• Required— the number of documents required for training a model.
• Number of ineligible/eligible documents:
  • Ineligible documents— documents that do not meet the criteria for processing
  • Eligible documents— documents that meet the required criteria and can be used for model training
• Analyze your data by clicking the Reanalyze data button.

Training Data Table

The Training Data table shows all documents available for use as training data for your model. You can filter the contents of the table by training document ID, group ID, number of pages, submission date, training status, and scheduled deletion.

The Training Data table contains the following columns:

• Doc ID— the ID number of your document
• Group ID— the ID number of the document’s group.
• Importance— “High” or “Low,” depending on the results from the training data analysis
• Pages— the number of pages in the specific document
• Date Modified— the last time changes were made in the document
• Training Status — the training status of your document:
• Scheduled Deletion — the date the document is scheduled for deletion.

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.
• 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 and then uploaded to this instance.
• Proj auto— Displays the predicted automation based on the Test Target Accuracy.
• Fields Layout / trained— Displays the number of fields in the current live layout vs. the number of fields the model was trained on.
• Docs Trained— The total number of documents used for training the model.
• Last Deploy— The last date and hour the model was deployed.