Maria Irena Szawerna, Simon Dobnik, Ricardo Muñoz Sánchez, and Elena Volodina. 2025. The Devil’s in the Details: the Detailedness of Classes Influences Personal Information Detection and Labeling. In Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025), pages 697–708, Tallinn, Estonia. University of Tartu Library. https://aclanthology.org/2025.nodalida-1.70/
Additional ways to cite the dataset.
A model based on KB/bert-base-swedish-cased trained to detect personal information, especially in learner essays. This variant differentiates between 7 general categories and differentiates between beginning and inside.
This model does not guarantee the detection of all personal information in the text. Never use it without human supervision (human-in-the-loop). The model performs noticeably worse on texts that are not student essays.
Personal Information detection
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KB-bert-base-swedish-cased_PI-detection-general-iob
The model is hosted on HuggingFace and can be easily accessed e.g. using their Python library.
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109.69 KB | GPL-3.0 |