TDM for Identification Models

• Updated on Jan 21, 2026
• Published on Sep 24, 2025
• 13 minute(s) read

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).

• You can access Layout Management from the Model Management page, as shown in the image below:

In the Models section, go to the Identification tab and click the name of your layout to access its Model Management page.

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 the layout of this model'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.
• 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 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.

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.

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

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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.

Training Data Table

The Training Data table allows you to review, organize, and manage the documents used to train your model.
Filter the table’s contents by:

• Status— training document status
• Date Modified— set a specific date range to view the training documents
• Group— filter by groups, formed after Training data analysis.
• Training Eligibility— filter by document eligibility. To learn more, see Document Eligibility Filtering
• Importance— high or low importance documents. Learn more in Training Data Curator.
• Has Anomalies— filter by anomalies detected. Learn more in Labeling Anomaly Detection.

You can search for documents by their IDs.

In v42.2 and later, you can also use tags to organize and filter training documents.

Search and filtering

The Group filter supports multi-select, making it easier to review and compare documents across several groups. Additionally, you can filter by multiple statuses to make the review process more flexible and efficient.

You can also search training documents by their file names.

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.

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The Training Data table contains the following columns:

• ID— the ID number of your document
• File Name— the original document name. Allows you to manage training data faster.
• Group— the ID number of the document’s group. This ID is available after training analysis.
• Priority— “High” or “Low,” depending on the results from the training data analysis
• Pages— the number of pages in the specific document
• Modified— the last date changes were made in the document
• 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.
• Deletion — the date the document is scheduled for deletion.
• \[v42.2 and later] Tags— Labels used to organize training documents. Tags can be added manually and used to filter the Training Data table.

You can assign tags directly from the Training Data table by hovering over the Tags cell for a document and selecting an existing tag or creating a new one.

• Manual tagging— Hover over the Tags cell to reveal a + button. Use it to select an existing tag or create a new one.

• Tag filtering— Filter documents in the Training Data table by tag to find relevant items quickly.

• Tag import— Tags included in imported training data are automatically imported.

• Tag naming rules— Tags cannot contain semicolons ( ; ) or spaces. Spaces are replaced with underscores (_).

• Unused tags— Tags that are not assigned to any document are automatically deleted.

• 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.

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. Learn more in our Evaluating Model Training Results article.
• 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.
• Actions — The options in this menu allow you to take the following actions on a model version:
  • Deploy
  • Undeploy
  • Download

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 entering a name in the search box.
• Sorting — Sort the table's contents by clicking on the names of the columns.

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

A tool used to annotate, manage, import, and export training documents. It is also used to train models by working directly with the training data (“ground truth”) obtained from each document in the training set.

A configuration within Hyperscience designed to process documents where fields and table cells are present, but their positions can vary among documents. Unlike Structured layouts, Semi-structured layouts do not rely on fixed field locations. Instead, a model is trained to find the fields and cells based on provided training examples.

A tool in TDM that analyzes your training data to compute the importance of each training document and identify issues such as missing labels, overlapping fields or columns, or inconsistent annotations. This analysis helps you prioritize which documents to annotate and ensures clean, accurate data before you train a model.

The predicted automation based on the desired target accuracy. The projection is derived from the model’s training data. The system automatically ensures that the same data is not used for both projections and training.

Automation refers to the processing of data without the need for human intervention.

A setting specified by the user. It indicates the desired overall system accuracy, including tasks performed by humans. It allows you to evaluate how well the system is expected to perform.

A list of values in a table that are of the same type of information, like names or prices, with one value per row. In simple tables, columns usually appear as vertical sections. However, in more complex tables, columns may not follow a vertical layout but still represent the same kind of data across rows.

An iteration of a layout that reflects the state of a layout at a given point in time. You can create new versions and restore older ones based on your needs.

Quality Assurance (QA) is a process that ensures the accuracy and reliability of system outputs. In Hyperscience, QA tasks allow users to review and correct errors in classification, identification, VLM extraction and transcription. Documents may enter QA after processing through Submissions, depending on the workflow configuration.

The range of uncertainty in the system's estimate of accuracy. It shows you how much the estimate may differ from the true value. The smaller the margin of error is, the more confident the system is in its estimate.

Automation Rate refers to the extent to which a machine can process data independently without requiring human supervision. It represents the proportion of extracted data with confidence scores exceeding a specified threshold. This threshold is determined by the level of accuracy you want the extracted data to have.

A group of documents used to help the system learn or improve. Datasets are used for training, testing, or evaluating how well the system reads and extracts information.

A collection of documents in the Training Data Curator used to organize training data for machine learning models. Groups help you manage, annotate, and track documents based on specific use cases, such as invoice processing.

A setting that allows you to remove PII from submissions to enhance security or comply with organizational policies. In Hyperscience, enabling this feature deletes all document image data, including extracted data and the original and processed images.

Annotation refers to a user-provided input that defines the correct prediction for a given machine learning task. Annotations are used to train supervised machine learning models.

A separate machine dedicated to handling resource-heavy tasks like training Identification models. It operates independently and connects to the main application through the API.