TDM for Identification Models

• Published on Jul 16, 2025

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 own 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.

In v41.2 and later, you can access Layout Management from the Model Management page, as shown in the image below:

The Actions menu in the page’s upper-right corner allows you to:

• Train new model
• Upload model
• Upload training documents
• Download training documents

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. Learn more in Requirements for Training a New Model.
  • 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. Learn more in our Evaluating Model Training Results article.

• 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.

• For example, if you train a model with 5 fields or columns but remove a field or a column from the current layout version, the numbers will be 4 / 5 (i.e., the layout has 4 fields or columns, but the model was trained on 5).

• Trained— date the model was trained.

• Deployed— date the model was deployed.

If the requirements for training a model are not met, then the model summary card will display the status “Reqs not met,” and a View training data button will appear on the card.

If your model is ready for training, the status “Ready to train” will appear on the model summary card. You’ll be able to start training by clicking the Train button located on the right-hand side of the card.

If you want to cancel your training, you can click the Cancel training job button next to the status of your model in the model summary card.

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 records. 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.

• Adjust the Target Accuracy percentage by clicking the up and down arrows.

The chart will display the projected automation of your model with the target accuracy you specify. You can determine the target accuracy value that best meets your needs by entering test values.

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).

The Identification Report is available only for Field Identification models.

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.

• You can also see the Margin of Error, the calculation points, and the automation rate for the selected period.

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 located at the top of the chart, or Machine Field ID or Manual Field ID located below the chart.
• Download the data as a CSV file by clicking the Download CSV button located on the right-hand side of each chart.

Field Identification Accuracy chart

The Field Identification Accuracy chart displays the percent accuracy for the selected time. You can see:

• Field Identification Accuracy
• Manual Accuracy
• Target Accuracy

Field / Table Level Automation

The Field / Table Level Automation card displays the automation percentage of the fields or columns your model was trained on:

• Field Name — displays the name of the field in your layout
• Machine Identified or Machine Identified Cells- indicates the number of values identified by the machine for that field or for that column
• Total or Total Cells — the total number of values identified for this field or column
• Automation Rate — the percentage of automation for the specific field

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. These documents do not include the minimum required for training to begin. Our recommendation is 100 documents.

• Required— the number of documents required for training a model. The default number is 100. If you would like to change it, contact your Hyperscience representative.

• 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

• Number of added or removed documents since the last training data analysis

• Number of groups discovered during the training data analysis

• Number of documents with potential anomalies

• 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. You can also search for documents by their IDs.

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

• Remove training documents– remove the selected training documents and their associated annotations
• Edit training status– change the training status of the selected training documents that have been annotated. All unannotated training documents that you’ve selected will keep their current status.

Model History table

The Model History table, located at the bottom of the Model Management page, 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 and ensures you’re working with the most recent model version.
• 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. Numbers in parentheses show the difference between them.
• Docs Trained— The total number of documents used for training the model.
• Last Deploy— The last date and hour the model was deployed.
• Actions— The options in this menu allow you to take the following actions on a model version:
  • Deploy
  • Undeploy
  • Download

In v40.2 and later, you can find specific records in the table in the following ways:

• Filtering — Filter the contents of the Model History table by creation date, last-deploy date, source, and trainer version. Click Filter and select the criteria that match what you’re looking for.
• Searching — Search for a model version by name by entering a name in the search box.
• Sorting — Sort the table's contents by clicking on the names of the following columns:
  • Name
  • Date created
  • Version
  • Source
  • Last deploy

Additionally, you can choose which columns are included in the table by clicking the menu next to the Filter drop-down list and clicking the Manage columns… option.

To train a Semi-structured model using TDM, see Training a Semi-structured Model.