<p>In our case here we have a very skewed dataset with <strong>200+ classes</strong> and <strong>20%</strong> of the classes containing <strong>80% of all data</strong>. </p>

<p>In our data, even with this highly skewed data, we have a <strong>clear definition</strong> of the texts inside our categories. </p>

<p><strong>Example</strong>:
Text of the Majority Class: "<em>Hey, I need a <strong>computer</strong> and a <strong>mouse</strong> to open the <strong>internet</strong> and post a <strong>programming</strong> answer in <strong>Stack</strong> <strong>Overflow</strong></em>"</p>

<p>Text of the Minority Class: "<em>Hey, could please give me the following items: <strong>Eggs</strong>, <strong>lettuce</strong>, <strong>onions</strong>, <strong>tomatoes</strong>, <strong>milk</strong> and <strong>wheat</strong>?</em>"</p>

<p>As FastText deals with WordNGrams and hierarchical split if you have a <strong>very well defined category</strong> as my case above, the imbalance it's not a problem because of the nature of the algorithm. </p>

<p>Reference: <a href="https://arxiv.org/abs/1607.01759" rel="nofollow noreferrer">Bag of Tricks for Efficient Text Classification</a> - Armand Joulin, Edouard Grave, Piotr Bojanowski, Tomas Mikolov</p>
How to handle unbalanced label data using FastText?
<p>In FastText, I have unbalanced labels. What is the best way to handle it?</p>