---
title: Activate “Not for Training” to Help Keep the Machine Learning Models in Shape
description: Learn how and when to activate “not for training” to avoid adding false training data to our machine-learning models.
---

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# Activate “Not for Training” to Help Keep the Machine Learning Models in Shape

## Learn how and when to activate “not for training” to avoid adding false training data to our machine-learning models.

### How does it work?

Every document validated by a user is automatically added to the training data, which the machine learns from. Wrong validations lead to false training data, which in turn reduces extraction quality. To correct incorrect training data, you can either reset and re-validate the document or remove it from the training data. Documents can be removed **before**, **during**, or **after** validation.

#### Before validation

Documents can be uploaded with the **"not for training"** flag during the upload process. 

#### During Validation

If a document needs to be removed from training during validation, users can click on the **"Tags & Flags"** tab and select the checkbox to remove the document from training.![](https://support.parashift.io/hs-fs/hubfs/image-png-May-29-2024-09-21-09-2121-AM.png?width=657&height=248&name=image-png-May-29-2024-09-21-09-2121-AM.png)

#### Overview Section

Documents can also be removed from training via a bulk action directly from the Overview section. Select the documents and add them to or remove them from training.

 ![not for training](https://support.parashift.io/hs-fs/hubfs/not%20for%20training.png?width=670&height=146&name=not%20for%20training.png)

You can also add or remove documents using the Training icon. 

**![](https://support.parashift.io/hs-fs/hubfs/image-png-Aug-06-2026-08-54-31-0710-AM.png?width=1728&height=198&name=image-png-Aug-06-2026-08-54-31-0710-AM.png)**

**Using the “Not for Training” Flag During Validation**

Whenever you’re about to finish the validation of a document, all fields should be validated properly according to their requirements. If you’re ever unsure whether you’ve fulfilled the requirements of a field, or if you’re working on an integration and decide to confirm the extraction results without fully checking them (to save time), you can activate the **“not for training”** flag.  
Once this flag is set, no training export will occur, and no false training data will be delivered to our machine-learning models.

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