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Why

Tagging a dataset is done to create a set of training examples for text classification problems.

Our goal in the tagging process is to attach a label to as many training examples as possible.

Terminology

• Label - The correct classification associated with an example. When humans add classes to examples manually, the classes that they add are labels 
• Tag - A classification for an example that is guessed by a model

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• Facet Name - The Facet that stores the labels added by humans
• Tag Facet Name - The Facet that stores the tags predicted by the model
• Labels to use - The options for different classes, if they have not already been predicted by a model.
  • For example, if you have the classes "pos" and "neg", you can fill in this config option with the value "pos,neg" to tell that to the widget
• Show bulk tagging controls - If selected, a black bar will appear at the top of the widget, with the option to label the top 10 examples shown with a single click.

If training and inference jobs are already set up, you will see the prediction strength for each class for each example in the project (the darker a class shows up, the more strongly the model predicts that label). At this point, you are ready to start labeling examples and training the model.

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Usage

It is typically easiest to use the search bar to find good examples to tag first. Any type of search can be used along with the data labeling widget, so there are lots of clever ways of finding good examples for each class to start with. Such as:

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