Facebook recently open-sourced word vectors in 89 languages. However these vectors are monolingual; meaning that while similar words within a language share similar vectors, translation words from different languages do not have similar vectors. In a recent paper at ICLR 2017, we showed how the SVD can be used to learn a linear transformation (a matrix), which aligns monolingual vectors from two languages in a single vector space. In this repository we provide 78 matrices, which can be used to align the majority of the fastText languages in a single space. Word embeddings define the similarity between two words by the normalised inner product of their vectors. The matrices in this repository place languages in a single space, without changing any of these monolingual similarity relationships. When you use the resulting multilingual vectors for monolingual tasks, they will perform exactly the same as the original vectors. To learn more about word embeddings, check out Colah's blog or Sam's introduction to vector representations.