This paper presents a fast and efficient text classification method called fastText. The authors show that fastText can achieve performance comparable to deep learning models but is significantly faster in both training and evaluation. fastText can train on over a 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 represents sentences as a bag of words and trains a linear classifier, such as logistic regression or SVM. To handle large output spaces, the model uses a rank constraint and a fast loss approximation. It also incorporates n-gram features to capture local word order information. The model uses a hierarchical softmax to reduce computational complexity during training and testing. The authors evaluate fastText on two tasks: sentiment analysis and tag prediction. On sentiment analysis, fastText achieves performance comparable to deep learning models, with a significant speed advantage. On tag prediction, fastText outperforms a frequency-based baseline and a similar model called Tagspace, achieving better accuracy and faster inference. The model is trained using stochastic gradient descent with a linearly decaying learning rate. It is implemented on a CPU with multiple threads, making it significantly faster than deep learning models trained on GPUs. fastText is also more efficient in terms of memory usage and can handle very large datasets. The authors conclude that fastText is a simple and efficient baseline for text classification that can compete with deep learning models in terms of performance while being much faster. They provide their code for the research community to build upon their work.This paper presents a fast and efficient text classification method called fastText. The authors show that fastText can achieve performance comparable to deep learning models but is significantly faster in both training and evaluation. fastText can train on over a 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 represents sentences as a bag of words and trains a linear classifier, such as logistic regression or SVM. To handle large output spaces, the model uses a rank constraint and a fast loss approximation. It also incorporates n-gram features to capture local word order information. The model uses a hierarchical softmax to reduce computational complexity during training and testing. The authors evaluate fastText on two tasks: sentiment analysis and tag prediction. On sentiment analysis, fastText achieves performance comparable to deep learning models, with a significant speed advantage. On tag prediction, fastText outperforms a frequency-based baseline and a similar model called Tagspace, achieving better accuracy and faster inference. The model is trained using stochastic gradient descent with a linearly decaying learning rate. It is implemented on a CPU with multiple threads, making it significantly faster than deep learning models trained on GPUs. fastText is also more efficient in terms of memory usage and can handle very large datasets. The authors conclude that fastText is a simple and efficient baseline for text classification that can compete with deep learning models in terms of performance while being much faster. They provide their code for the research community to build upon their work.