This paper introduces fastText, a simple and efficient baseline for text classification. fastText is designed to be much faster than deep learning classifiers while achieving comparable accuracy. The authors demonstrate that fastText can train on over one billion words in less than ten minutes using a standard multicore CPU and classify half a million sentences among 312K classes in less than a minute. The model uses a linear classifier with a rank constraint and a fast loss approximation, leveraging efficient word representation learning techniques. The paper evaluates fastText on two tasks: sentiment analysis and tag prediction on the YFCC100M dataset. Results show that fastText outperforms or matches the performance of state-of-the-art deep learning methods in terms of accuracy, while being significantly faster in both training and testing. The authors also discuss the scalability and efficiency of fastText, highlighting its potential for large-scale applications.This paper introduces fastText, a simple and efficient baseline for text classification. fastText is designed to be much faster than deep learning classifiers while achieving comparable accuracy. The authors demonstrate that fastText can train on over one billion words in less than ten minutes using a standard multicore CPU and classify half a million sentences among 312K classes in less than a minute. The model uses a linear classifier with a rank constraint and a fast loss approximation, leveraging efficient word representation learning techniques. The paper evaluates fastText on two tasks: sentiment analysis and tag prediction on the YFCC100M dataset. Results show that fastText outperforms or matches the performance of state-of-the-art deep learning methods in terms of accuracy, while being significantly faster in both training and testing. The authors also discuss the scalability and efficiency of fastText, highlighting its potential for large-scale applications.