# Relative regularization coefficients: ARTM & TopicNet using

*By reading this tutorial you will know how to use TopicNet and ARTM libraries for building topic models with regularizers.*

## Why do we need regularizers?

Building the topic model for a collection of documents helps us to recognize the topics of the documents and determine the words describing these topics. If we want the model to have certain properties, **regularizers** are the best solution.

## What about the relative coefficients of regularization?

The first step of building a regularized topic model is to select the hyperparameter — regularization coefficient **τ **showing how strong the regularizer* *affects the model. The optimization of this coefficient is a challenging task as its value depends on various features including the dataset size. *Now just imagine: *firstly, you were selecting **τ **for a collection, then you collected a bigger dataset so you have to select **τ **again**. **That doesn’t sound like a productive activity, does it? Here come to rescue** the relative regularization coefficients.**

## How do we use them?

The relative regularization coefficient shows how many times the regularizer affects the model more strongly than the collection of documents itself. The relative coefficients **λ **transform to absolute **τ **by this formula:

If we designate parameter **λ **while announcing regularizer, **τ **will show the relative degree of regularization, not an absolute one.

## ARTM using

`artm.DecorrelatorPhiRegularizer(name=’DecorrelatorPhi’, gamma=0, tau=2, …)`

*Set the decorrelation regularizer which will affect the model twice as strong as the text (with equal strength for all topics).*

## TopicNet using

`rel_toolbox_lite.handle_regularizer`

*This method takes your regularizer, model and some other things and transforms the regularization coefficient from absolute to relative by the above formula.*

Congratulations, now you are a happy possessor of secret knowledge — relative regularization coefficients in topic models!