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Important :
Communications Mining is now part of UiPath IXP. Check the Introduction in the Overview Guide for more details.
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Communications Mining user guide

Last updated Aug 1, 2025

Model rollback

The model rollback feature allows you to revert back to a previous version of your model. This way, you can reset the training data, for both label and general field annotations, to the annotations used to train this model version.

Important: You can only roll back to pinned model versions.

Using model rollback

The model rollback icon is available on the Models page, on all pinned model versions.

To use the model rollback feature, proceed as follows:
  1. Select the rollback icon on the model version you want to revert back to.
    Note: The current trained model version will automatically be pinned as a backup but any annotations captured by a model version that is currently still training will be lost.



  2. Once you select the rollback button, a pop-up window appears as a reminder that you are recommended to allow the current model version to finish training before rolling your model back. To proceed, select Reset.


If the model rollback has started successfully, a banner will appear on the page.



What happens during the model rollback

While the model is rolling back, you cannot modify the dataset. This means that you cannnot train your model during this time, and apply any labels or general fields to messages.

A warning indicator will show up at the top, informing you that the model is being rolled back.



If you try to modify your dataset, the following banner will appear on the page, and any messages we try to annotate will not have the label or general field applied to it until the model rollback has complete.



Although the rollback feature helps you roll back to a previous version of a model, if you have made any major mistakes in our model training, you should not rely too much on it.

Instead, make sure that you follow the proper model training methodology correctly the first time, as this can save us time in the long-run.

  • Using model rollback
  • What happens during the model rollback

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