GloVe type word vectors (embeddings) for Lithuanian. Delfi.lt corpus (~70 million words) and StanfordNLP were used for training. The training consisted of several stages: 1) the vocabulary was compiled, eliminating words the the frequency less than 5; 2) word co-occurrence matrix was generated with window size of 5; 3) this matrix was randomly shuffled; 4) word vectors were generated (100 iterations, 200 dimensions). The final result consists of 331 203 unique word vectors.