Expanded fastText library now fits on smaller-memory devices

This post details the expansion of the fastText library to support smaller-memory devices by reducing model memory usage from gigabytes to hundreds of kilobytes. This was achieved through collaboration with FAISS and the "FastText.zip: Compressing Text Classification Models" paper, enabling deployment on smartphones and Raspberry Pi. The post also highlights fastText's efficiency, speed (1,000 to 10,000 times faster than neural networks), and its use of hierarchical softmax and bag-of-words/n-gram models for efficient text classification and word representation learning. Two self-paced tutorials are provided for supervised text classification and word representation learning.