Many Natural Language Processing applications nowadays rely on pre-trained word representations estimated from large text corpora such as news collections, Wikipedia and Web Crawl. In this paper, we show how to train high-quality word vector representations by using a combination of known tricks that are however rarely used together.

The main result of our work is the new set of publicly available pre-trained models that outperform the current state of the art by a large margin on a number of tasks.