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/20.500.12185/631Static word embedding model based on the Global Vectors architecture (Pennington et al., 2014). The embeddings provide real-valued vector representations for Setswana text.Training data: Paragraphs: 515,961; Token count: 14,518,437; Vocab size: 52,155; Embedding dimensions: 400;tnModules74.15MB (Zipped)