TDM for Identification Models

• Updated on May 5, 2026
• Published on Mar 31, 2025
• 9 minute(s) read

Training Data Management (formerly Keyer Data Management) includes tools for controlling and managing Identification models performance. In this article, you will learn how to navigate TDM for Identification Models and its features. Learn how to use them together in Training a Semi-structured Model.

TDM features

Training Data Management includes:

• Document Eligibility Filtering— indicates whether a document is eligible for training, based on internal checks in the application and our machine learning logic. It provides additional information about documents that were excluded from the training set. To learn more, see Document Eligibility Filtering.

• Training Data Curator — labels each training document as having high or low importance. The importance is calculated by determining which data would best contribute to the model’s performance. For more information, see Training Data Curator.

• Labeling Anomaly Detection for Fields and Tables— identifies potential discrepancies in the training datasets before running model training. Once the annotations are ready, the user can analyze the data to find inconsistencies and ensure a top-performance locator model. See Labeling Anomaly Detection for more details.

Learn how to use Training Data Management in Training a Semi-Structured Model.

Navigating TDM for ID models

Each locator 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 located in the upper-right corner of the page.

Learn how to navigate through TDM for ID models below.

Model Summary Card

Navigating Model Summary card

The Model summary card displays the Live and the Candidate models for this layout. 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. It will redirect you to the Training Data table.

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.

• Expand it by clicking the arrow ().

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.

• 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. Hover over the Accuracy value to see the MoE and the Calculation points.
  • Hover over the Accuracy value to see the MoE and the Calculation points.

Calculation points are the total fields used to calculate accuracy. They represent the number of evaluated fields. For instance, an accuracy of 50% could come from 1/2 or 400/800 evaluated fields. Learn more in our Accuracy article.

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.

• Hover over the chart’s data to see the specific date and the number of fields identified by the machine or by a human.

• 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 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
• You can sort each column and select the number of rows to display on the page.

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

Learn more about eligibility in Document Eligibility Filtering.

• 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.

Learn how to optimize your data and achieve better model performance by using Training Data Analysis, as described in Step 4 of Training a Semi-structured Model.

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.

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. This ID is available after training analysis.
• 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:
  • Always— The document will always be used to train the model.
  • Auto— The document will be used to train the model until its scheduled deletion, based on the PII Data Deletion settings for your instance.
  • Never — The document will never be used in future model training, even if it hasn’t been deleted as part of the PII Data Deletion.
  • Loading— The document is currently being pre-processed and prepared for annotation.
  • Ready to annotate— The document has been uploaded successfully but has not been annotated yet.
• Scheduled Deletion — the date the document is scheduled for deletion.
• Actions— Each document has its own Actions link:
  • Annotate — available for documents that do not have any annotations.
  • Edit Annotations — available for documents that have already been annotated.

Learn how to annotate your documents to achieve a top-performing model in Step 5 in Training a Semi-structured Model.

Model History Table

The Model History table is located at the bottom of the Model Management page. You can upload a model from another instance, run training, and adjust your Test Target Accuracy. The table displays the following columns:

• Name — the name of the last available model for this layout
• Date Created— the time and date of the last training of the model
• Version — the version of the trainer used for this model training
• Source:
  • Internal— when the model was trained locally in this instance
  • Upload— when the model was imported from another instance
• Proj auto— indicates the projected automation based on the Test Target Accuracy
• Fields Layout / trained or Column Table / trained — The number of fields or columns in the current live version of your layout / The number of fields or columns used for training the model.

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

• Docs trained— the number of documents used for training the model.
• Last deploy— the date the model was deployed.
• Actions— options to undeploy, reject a candidate, download, or deploy the specific version of the model.

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