  Words embedding (distributed word vector representations) have become an
essential component of many natural language processing (NLP) tasks such as
machine translation, sentiment analysis, word analogy, named entity recognition
and word similarity. Despite this, the only work that provides word vectors for
Hausa language is that of Bojanowski et al. [1] trained using fastText,
consisting of only a few words vectors. This work presents words embedding
models using Word2Vec's Continuous Bag of Words (CBoW) and Skip Gram (SG)
models. The models, hauWE (Hausa Words Embedding), are bigger and better than
the only previous model, making them more useful in NLP tasks. To compare the
models, they were used to predict the 10 most similar words to 30 randomly
selected Hausa words. hauWE CBoW's 88.7% and hauWE SG's 79.3% prediction
accuracy greatly outperformed Bojanowski et al. [1]'s 22.3%.
