Creative Commons Attribution 4.0 International (CC-BY 4.0)Roald EiselenRico KoenAlbertus KrugerJacques van Heerden2023-07-282023-05-012023-07-282023-05-012023-05-01https://hdl.handle.net/20.500.12185/659Static word embeddings for the Skipgram flavour of the word2vec (w2v) architecture (Mikolov et al., 2013). The embedding provides real-valued vector representations for Siswati text.Training data: Paragraphs: 299,112; Token count: 4,436,576; Vocab size: 41,685; Embedding dimensions: 600;ssModules89.24MB (Zipped)