ro-stories dataset

Dataset available at: https://huggingface.co/datasets/readerbench/ro-stories

ro-auth-detect

content

• feature_extraction_selection.py - to extract ReaderBench Textual Complexity Indices and select top top 100 indices via Kruskal-Wallis Mean Rank
• ensemble_learning_ft.py & ensemble_learning_pp.py - combines 7 ML models with soft vote function to generate improved predictions (ft-full text; pp-paragraphs)
• hybrid_transformer_ft.py & hybrid_transformer_pp.py - hybrid transformer model, combining text inputs and linguistic features to predict the author of a given text (ft-full text; pp-paragraphs)
• stats directory contains the results of McNemar's and Cochran's Q statistical tests

usage

• the code was tested using Google Colab Pro, which is recommended for visualizations
• small adjustments to the files may be required to execute them as Python scripts
• detailed instructions and requirements are provided within each file

paper

Cite this as: Nitu M, Dascalu M. Authorship Attribution in Less-Resourced Languages: A Hybrid Transformer Approach for Romanian. Applied Sciences. 2024; 14(7):2700. https://doi.org/10.3390/app14072700