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NCHLT isiXhosa RoBERTa language model
Contextual masked language model based on the RoBERTa architecture (Liu et al., 2019). The model is trained as a masked language model and not fine-tuned for any downstream process. The model can be used both as a masked LM or as an embedding model to provide real-valued vectorised respresentations of words or string sequences for isiXhosa text.
Roald Eiselen
Roald.Eiselen@nwu.ac.za
North-West University; Centre for Text Technology (CTexT)
Creative Commons Attribution 4.0 International (CC-BY 4.0)
isiXhosa
Roald Eiselen
Rico Koen; Albertus Kruger; Jacques van Heerden
https://hdl.handle.net/20.500.12185/644
Text
Modules
Language model
Training data: Paragraphs: 718,751; Token count: 13,190,962; Vocab size: 30,000; Embedding dimensions: 768;
235.81MB (Zipped)
NCHLT Text IV
Python
Web; Government Documents
xh
2023-07-28T08:11:54Z; 2023-05-01
2023-07-28T08:11:54Z; 2023-05-01
2023-05-01


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  • Resource Catalogue [349]
    A collection of language resources available for download from the RMA of SADiLaR. The collection mostly consists of resources developed with funding from the Department of Arts and Culture.

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