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The datasets(Simlex and WordSim) contain pairs of Setswana and Sepedi words that have been assigned similarity ratings by humans to measure semantic relatedness. The word-pairs(Simlex and WordSim) are manually translated from English to Setswana and Sepedi. The evaluation task aims to find the degree of correlation between the scores provided by the model and the human rating, the score of the model is collected by computing the cosine similarity of corresponding vectors for word pairs.
- Zenodo Data Repository - Link to the data repository.
- Vukosi Marivate - @vukosi
- Valencia Wagner
- Mack Makgatho
- Tshephisho Sefara
See also the list of contributors who participated in this project.
To appear in conference proceedings
@article{Makgatho_Marivate_Sefara_Wagner_2022, title={Training Cross-Lingual embeddings for Setswana and Sepedi}, volume={3}, url={https://upjournals.up.ac.za/index.php/dhasa/article/view/3822}, DOI={10.55492/dhasa.v3i03.3822}, number={03}, journal={Journal of the Digital Humanities Association of Southern Africa }, author={Makgatho, Mack and Marivate, Vukosi and Sefara, Tshephisho and Wagner, Valencia}, year={2022}, month={Feb.}}
Dataset is licensed under CC-BY-4.0 This project is licensed under the MIT License - see the LICENSE.md file for details