TDM for ORCA VLMs

TDM for ORCA VLMs

Training Data Management (TDM) is where you prepare and manage the data used to train your models. In TDM, you review and annotate documents, build your training dataset, and improve model performance for your specific use case.

In this article, you’ll learn how TDM allows you to prepare, annotate, and manage training data for specialized ORCA VLM models.

Prerequisites

Before you start, ensure the following requirements are met:

Model details page

The Model details page is where you manage and evaluate your model. It includes three main tabs:

Overview

The Overview tab provides a summary of the deployed model, the candidate model, the health of the training data, and projected automation.

Model summary card

The Model summary card provides insights into all models trained for a specific model definition. See the table below to learn about the displayed details:

| Field | Description | Notes | | State | Shows the model’s state. | - A Live model is a deployed model.
- A Candidate model is an undeployed model. It allows you to review model performance and decide whether the model should be deployed or retrained with additional data.
- An Inactive model is a candidate or an undeployed model. | | Projected automation | Displays the performance of the model that’s currently live. | The predicted automation based on the desired target accuracy. The projection is derived from the model’s training data. | | Test Target Accuracy | The accuracy percentage used to calculate the projected automation. | Indicates the desired overall system accuracy. | | Trained | Date the model was trained. | | | Layout version | The layout version used for this model definition. | Always use the latest locked layout version. |

Training data card

The Training data card displays information about your dataset. See the table below for more information:

| Field | Description | Notes | | Training status | Indicates the status of your model based on the training data. | - Ready to train
- Reqs not met
- Training failed — the model training process failed.
- Training in progress— model is currently training. | | Total documents | The number of annotated documents for this model. | |

Projected Automation

The Projected Automation chart displays the performance of the currently live model, compared to the candidate one.

The chart displays how the target accuracy affects the automation. The lower the accuracy, the higher the automation, and vice versa.

Training Data

The Training Data tab lists all training documents and allows you to annotate and manage them.

Training Data tab

Upload, manage, and annotate training documents.

Tagging documents
You can organize and manage the training documents more efficiently in TDM for ORCA VLMs by adding tags. This feature allows you to add, filter, import, and export tags for documents, making it easier to categorize and find the information you need.

VLM annotation

VLM annotation experience

While the ORCA VLM works out of the box, you can improve its performance by training it on your specific data, using annotated documents.

History

The History tab lists all models trained for the selected model definition. From this tab, you can deploy, undeploy, or reject models, and view detailed information for each one.

History tab

View and manage your trained models

| Column | Description | Notes | | Name | Model name | | | State | Model state | - A Live model is a deployed model.
- A Candidate model is an undeployed model.
- An Archived model is a model that is no longer in use.
- An Inactive model is a candidate or an undeployed model. | | Compatibility | Compatibility of the most recently live model for this definition. | - Orange indicates the current application version.
- Green indicates one version ahead of the current application version.
- Blue indicates two versions ahead of the current application version. | | Layout version | The layout version for this model. | | | Source | Where the model was trained. | - Internal— trained in the current instance.
- Upload— trained in another instance and uploaded to the current one.
- Base— ORCA base model |

During training, the system uses the ORCA base model and the annotated documents to learn patterns specific to your use case. The training process produces a candidate model, which can then be evaluated and deployed.

Training results

After training completes:

You can retrain the model by adding more annotated documents and running training again.

Deploying the candidate model

To start using the trained model: