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/20.500.12185/633Static word embedding model based on the Global Vectors architecture (Pennington et al., 2014). The embeddings provide real-valued vector representations for Tshivenḓa text.Training data: Paragraphs: 304,248; Token count: 7,363,713; Vocab size: 27,037; Embedding dimensions: 500;veModules47.82MB (Zipped)