Models Page

Updated on Nov 12, 2025
3 minute(s) read

Accessing the features mentioned in this article

Your access to some of the features mentioned in this article depends on your license package and pricing plan. To learn which features are available to your organization and how to add more, contact your Hyperscience representative.

The Model Management page displays a list of all models trained on this instance. In this article, you'll learn how to navigate the pages for different types of models.

To access the Model Management page, go to the Models section, and choose which type of models you want to see from the tabs:

Identification Models - Field ID or Table ID models
Classification Models
- Learn how to manage your Identification and Classification models in our Training Data Management article.
Text Classification Models
Transcription Models

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Classification Models table

The default tab that appears on your screen when you click Models is Classification.

You can filter the Classification models table by release with the Filter by release drop-down list, which is located on the right-hand side of the page.
Access the model management page for a particular model by clicking on its name in the table.
See the number of Classification models available in your instance.

The Classification models table contains the following columns:

Model— shows the name of your Classification model
Compatible Releases— indicates the number of releases that the Classification model can generate predictions for
Status— displays the model's current state (e.g., Needs Training or Live)
Date Deployed— the date the model was deployed.

Learn more about Classification models in TDM for Classification Models.

Identification Models table

Go to the Identification tab to view Field ID or Table ID models.

The Identification Models table lists all Semi-structured layouts available in your instance, along with their associated models.

The table has the following columns:

Model— shows the name of the model’s layout. This column is sortable.
Type— the type of locator models available for the layout: Field ID, Table ID, or both.
Model Status— the current status of the model. Possible statuses are:
- Needs Training— The model requires training and cannot be used for submission processing at this time.
- Live— The model is trained and deployed.
Training Status— indicates the current state of the model training (e.g., Pending, In Progress, Failed, Canceled, or Last trained on [date]).

Learn more about Identification models in TDM for Identification Models.

Text Classification Models table

Go to the Text Classification tab to access this table.

You can import or create a Dataset by clicking on the buttons located above the table. The number of Text Classification datasets appears at the top.

The Text Classification Models table provides the following information for each model:

Dataset Name
Date Created
Source UUID

Learn more in Text Classification.

Transcription Models tab

Transcription models are collections of fine-tuning models. Go to the Transcription tab to view all available fine-tuning models in your instance.

For each Transcription Model, the table provides the following information:

Transcription Model— the name of the model
Flows— the number of flows using the model. Clicking this number reveals a list of the flows.
Model Types— The types of fine-tuning models that have at least one QA record available to them (e.g., Latin Semi-Structured, Checkbox, Signature).

Currently, it is not possible to create new sets of fine-tuning models in the application. If the transcription models listed in the Model Library do not meet your needs, contact your Hyperscience representative.

Daily Autotraining - Daily auto-training of fine-tuning models is enabled by default. It retrains all Finetuning models daily based on the latest QA records. Note that the model training starts in the early morning (UTC).

If you are not obtaining high-quality results from Transcription Supervision and QA tasks, consider disabling daily autotraining to prevent it from affecting model performance. You can re-enable it after your results improve.

To enable or disable the daily training of your transcription models, click the Daily Autotraining enabled toggle.

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Learn more about transcription models in Managing Transcription Models.

A machine learning model that reads unstructured text—like comments, emails, or notes, and assigns them to predefined categories. This categorization helps automate decisions and organize freeform text based on business rules.

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 machine learning model that automatically extracts text from scanned document images. It supports both printed and handwritten text. When the model’s confidence in the extracted text is low, the system generates a Transcription task for human review to ensure accuracy. Learn more in Transcription models.

A process that improves the accuracy by using your data to adjust system thresholds automatically. It helps ensure the system makes more accurate predictions and flags uncertain results for review, reducing errors and improving overall performance.