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/20.500.12185/658Static word embeddings for the Skipgram flavour of the word2vec (w2v) architecture (Mikolov et al., 2013). The embedding provides real-valued vector representations for Sepedi text.Training data: Paragraphs: 292,594; Token count: 8,908,709; Vocab size: 13,357; Embedding dimensions: 400;nsoModules19.07MB (Zipped)