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/20.500.12185/628Static word embedding model based on the Global Vectors architecture (Pennington et al., 2014). The embeddings provide real-valued vector representations for Sepedi text.Training data: Paragraphs: 292,594; Token count: 8,908,709; Vocab size: 39,346; Embedding dimensions: 400;nsoModules55.89MB (Zipped)