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/20.500.12185/632Static word embedding model based on the Global Vectors architecture (Pennington et al., 2014). The embeddings provide real-valued vector representations for Xitsonga text.Training data: Paragraphs: 360,698; Token count: 7,357,764; Vocab size: 29,945; Embedding dimensions: 500;tsModules52.86MB (Zipped)