Expanded fastText library now fits on smaller-memory devices

What this post added

This post details the expansion of the fastText library to support smaller-memory devices by reducing model size to a few hundred kilobytes. This was achieved through a collaboration with the FAISS library and the implementation of compression techniques described in "FastText.zip: Compressing Text Classification Models." The post also highlights fastText's efficiency, speed, and ease of use for text classification and word representation learning, including its use of low-dimensional vectors, hierarchical softmax, and n-gram models. New pre-trained vectors in 294 languages and quick-start tutorials are also released.