Training a New Field Identification Model

Updated on Jul 15, 2025
Published on Mar 18, 2025
5 minute(s) read

Accessing this feature
Your access to the feature described 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.

There are two ways to train a Field ID model.

To manually train and deploy models, go to the Model Details page, and follow the instructions in this article.
To automatically train and deploy Field ID models, you can enable the Continuous Field Locator model improvement setting.
- For optimal performance, we recommend that you train models manually and disable the Continuous Field Locator model improvement setting. Only enable this setting if instructed to do so by a Hyperscience representative.
- See Identification Settings for more information about Continuous Field Locator model improvement.

To train and deploy models, go to the Model Details page. Once you determine a Semi-structured layout where you would like to train a model, there are two ways to get to the Model Details page:

Go to Library > Models, select Identification Models from the drop-down list at the top of the page, and then click on the name of the model.
Go to Layouts, click on the name of the layout, and then click on the name of the Identification Model on the Layout Details page.

To understand the requirements to train a model, see Requirements for Training a New Model.

Multiple Occurrences Field ID model

The Multiple Occurrences (MOs) feature helps you identify multiple instances of a field. Learn more about fields with various occurrences in Field Identification.

The Generic Engine type is used to train Field Identification, Table Identification, and Multiple Occurrences (MO) models. For more information, contact your Hyperscience representative.

Multiple Occurrences checkbox

The default Field ID model can predict multiple occurrences of fields. Users can now indicate whether a field needs annotation in multiple instances by selecting the Multiple Occurrences checkbox in the Layout Editor.

When this checkbox is selected, Multiple Occurrences annotations will be enabled, and the model will look for multiple instances of a specific field.
When this checkbox is deselected, you can annotate only a single instance of the field. The model will return only one result.

If you select the Multiple Occurrences checkbox for a field, annotate your dataset, and then deselect the checkbox, the annotations won’t be invalidated. Still, the Training Data Analysis will display anomalies for documents that have multiple instances of that field. Learn more about anomalies in Labeling Anomaly Detection.

Long-form (Unstructured) Extraction Field ID model

A GPU trainer is required in order to use Long-form Extraction (f.k.a. Unstructured Extraction). Contact your Hyperscience representative for more information. If you have an on-premise deployment of Hyperscience, you can also learn more by reviewing the "Enabling Trainers with GPUs" articles for Docker, Podman, and Kubernetes.

The default Field Identification models use a generic engine type that cannot extract data points from documents with unstructured text. To extract data points from Unstructured documents using a specific layout, you need to select the Long Form Extraction engine type for that layout before training the model. Follow the steps below to change the engine type of your layout:

Go to Library > Layouts and open your layout.
On the Configuration card, click the Edit button under the Engine Type setting.
Select Long Form Extraction from the drop-down menu.
Click Change Type.

Retraining required: Retrain your model after you’ve changed the engine type of the layout.

When training a model for Long-form Extraction, the following limits apply:

2,000 text segments per page
200 pages per document
200,000 text segments per document
100,000,000 text segments total

To initiate model training, follow the steps from the Initiating Model Training section below.

Initiating Model Training

On the Model Details page, you can see if you've completed enough QA or Field ID Supervision to initiate training. If you have not yet reached the minimum, you'll see the number of additional documents required to reach the minimum.

Once you've reached the minimum, train a model by clicking the Run Training button (if there is no previous model) or Actions > Run Training (if there is an existing model).
After initiating training, the system will show that the model is pending.

Monitor the Notifications in the top right of the application to keep track of model training jobs. To cancel a model training job, see Canceling or Retrying a Training Job.

Anomaly Detection

With the Anomaly Detection feature, the system analyzes your training data and flags potential anomalies in the annotations for you to review. When you review each flagged annotation, you can mark it as correct or edit the annotation. If you re-train a model after reviewing the anomalies, you will improve automation. You can manually initiate model training at any point, even if you haven’t reviewed all of the flagged anomalies.

For more information, see Labeling Anomaly Detection.

Additional Notes

If your deployment does not have a dedicated machine for training, document processing times will be severely delayed while the model trains. Without a dedicated machine, it is best to avoid processing documents while training models.
Initiating training on subsequent models for a Semi-structured layout is identical to initiating the first model. However, when you view the Model Details page, you'll see data associated with the live model in the Current Model section.
If you have PII deletion enabled on your system, or if you have imported a model from another instance, it is possible that you may not have enough documents to run training even if you have a live model. If this is the case, you'll need to wait until enough documents have been through QA or Field ID Supervision (increasing the sampling rate can reduce the wait time).

Always verify layout-version compatibility when switching between model versions. The Live version of a model always uses the most recent layout version, regardless of which layout version it was originally trained with. This configuration can lead to unexpected behavior, especially if changes were made to the layout after training (e.g. new fields, field-setting updates).

Multiple Occurrences (MOs) are used to identify multiple different instances of a field.