The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/e0d75aad-6cd1-45dd-a5ec-985e399337b4

Park, Jeongwoo; Liscio, Enrico; Murukannaiah, Pradeep K. (2024): Morality is Non-Binary: Building a Pluralist Moral Sentence Embedding Space using Contrastive Learning - models. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/e0d75aad-6cd1-45dd-a5ec-985e399337b4.v1

Dataset

• Artificial Intelligence and Image Processing
• Information and Computing Sciences

Contrastive Learning, Morality, Natural Language Processing, Sentence Embeddings

CC BY 4.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Jeongwoo Park , Enrico Liscio , Pradeep K. Murukannaiah

We train embedding spaces with the MFTC corpus, to see how an embedding space can learn the distribution of pluralist morality. We compare off-the-shelf, unsupervised, and supervised approaches, showing that a supervised approach is necessary. Here, you can find the models we trained with unsupervised and supervised approaches.

• 2024-01-30 first online, published, posted

4TU.ResearchData

pytorch_model.bin

https://doi.org/10.4121/a3e5abdf-f777-4105-be40-fb9b2e3fed92

• Hybrid Intelligence Center (a 10-year programme funded by the Dutch Ministry of Education, Culture and Science through the Netherlands Organisation for Scientific Research).

TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Intelligent Systems