Validate and annotate generated extractions

Note: This page explains how to validate field predictions on your extractions. You can decide when to stop training labels. Depending on the use case, your extractions have different performance requirements.

Give sufficient examples, so that the model can provide you with validation statistics. The validation statistics help you understand how well your extractions perform. Additionally, it allows you to fine-tune your extractions.

Review the results and:

Accept the extraction(s) if they are all correct.
Correct the extractions if there are any incorrect predictions.
Mark extractions as missing if they are not present in the message.
Configure any additional fields if any are missing that are required to enable end-to-end automation.

Why is fine-tuning important?

Fine-tuning allows you to use the annotations gathered to improve the performance of the extraction model.

It allows you to take the out-of-the-box model and enhance performance for your use cases.

When can you stop?

Stop once you provide at least 25 examples of label extractions for the model to use in its validation process. Check validation and see if the performance is sufficient, or whether more examples are needed.

Validating extractions - Explore tab

#	Description
1	If all the field predictions are correct, selecting the Confirm button allows you to confirm that the annotations are correct, in bulk.
2	To add or modify any fields that should have been present in the message, select +, next to the general field or extraction field section.
3	Checking this box allows you to confirm that a field annotation is correct, on an extraction level.
4	This shows what data point was predicted for a given field. If the prediction is incorrect, selecting the x button allows you to adjust the field with the correct one.
5	This shows the position in the message a data point(s) are predicted. A icon denotes when a general field is present on a message. An icon denotes when an extraction field is present on a message.
6	To add or modify any fields, hover next to the + button, on the respective general field or extraction field section.
7	To expand the fields displayed for General Fields or specific Extraction Fields, select the dropdown button.

Unconfirmed state extraction

The image below shows what an extraction looks like in its unconfirmed state. On the right pane, the extraction is marked as not confirmed, and the highlighting on the text itself has a lighter colour.

Note: The same concept applies for General fields as well.

Confirmed state extraction

The image below shows what fields look like in their confirmed state. On the right pane, the extraction is marked as Confirmed, and the highlighting on the text itself has a darker colour.

Note: The same concept applies for General fields as well.

Validating extractions – Train tab

Note:

The Extractions Train tab is in public preview.

To validate your extractions via the Train tab, follow these steps:

Go to Train.
Go to Extraction.
Select the label extraction that you want to validate.
Once you select the label extraction you want to validate, confirm whether the displayed message is an applicable example of the label.
Once you applied all the applicable labels, select Next: Annotate Fields.
Validating extractions in the Train tab experience is similar to validating extractions in Explore.
The main difference is that you can see the messages in training batches.
The confirm all and next button redirects you to the next message to annotate automatically.