Optimising a fastText model for better accuracyData Science by Sunny Srinidhi - December 3, 2019December 19, 20190 More in The fastText Series. In our previous post, we saw what n-grams are and how they are useful. Before that post, we built a simple text classifier using Facebook’s fastText library. In this post, we’ll see how we can optimise that model for better accuracy. Precision and Recall Precision and recall are two things we need to know to better understand the accuracy of our models. And these two things are not very difficult to understand. Precision is the number of correct labels that were predicted by the fastText model, and recall is the number of labels, out of the correct labels, that were successfully predicted. That might be a bit confusing, so let’s look at an example to understand it better. Suppose for a sentence
An intro to text classification with Facebook’s fastText (Natural Language Processing)Data Science by Sunny Srinidhi - November 25, 2019December 19, 20193 More in The fastText Series. Text classification is a pretty common application of machine learning. In such an application, machine learning is used to categorise a piece of text into two or more categories. There are both supervised and unsupervised learning models for text classification. In this post, we’ll see how we can use Facebook’s fastText library for some simple text classification. fastText, developed by Facebook, is a popular library for text classification. The library is an open source project on GitHub, and is pretty active. The library also provides pre-built models for text classification, both supervised and unsupervised. In this post, we’ll check out how we can train the supervised model in the library for some quick text classification. The library