Now we’ll retrain the model. You can choose between retraining texts from your uploaded file or upload annotated data as a new file.

Table of Contents

Option 1: Annotate selected instances from the uploaded file

Select instances to annotate from the uploaded text

Select Action:
Status
title Model
Select your model
Select Action:
Status
title Update
Select Mode:
Status
title request
and select how many instances you want to annotate.
Select sampler:
1. Random: Returns random text instances from your dataset
2. Margin
  Status
  colour Green
  title recommended
  : A metric to find uncertain instances for the underlying model
Then click the button “Request”.

Now you want to annotate the requested instances. To do that, proceed as following:

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You can repeat this procedure multiple times.

Upload a file that has a column with post annotations
Select the text column
Select the label column (post annotations)
Click “Update” to retrain the model. This process takes a little while. You may see a message “Model not ready. Come back later”.

After the model has been trained at least one time, the user will be able to monitor the evolution of the quality performance.

...

With your model selected, select action
Status
title inspect
.
After each iteration a new point will be drawn on the "estimated quality performance" line plot. You can deduce from it that, when the curve starts to flatten, the model quality is converging, and probably it's not going to learn much more.

Using a different dataset than the one the model was created with, you can evaluate the model’s performance.

Prepare your data file with the following columns:
1. Text: Your raw text instances
2. Label: Label annotations for each text
Select Action:
Status
title evaluate
Upload your file
Select the text column and label column
As a result, you’ll see a table with different performance metrics and a list of text instances where the predicted label and the annotated label mismatch.

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