Bag of Tricks for Efficient Text Classification

The paper introduces fastText, a deliberately simple and efficient baseline for text classification. Rather than relying on heavy deep learning machinery, it uses a fast linear classifier design that the authors show is often on par with deep learning classifiers in terms of accuracy while being many orders of magnitude faster for both training and evaluation.

The efficiency gains are dramatic: fastText can be trained on more than one billion words in less than ten minutes using a standard multicore CPU, and it can classify half a million sentences among 312,000 classes in under a minute. This combination of competitive accuracy and extreme speed made fastText a widely used practical baseline for large-scale text classification.