The model has been trained to predict for English sentences, whether they are formal or informal. Base model: roberta base Datasets: GYAFC from Rao and Tetreault, 2018 and online formality corpus from Pavlick and Tetreault, 2016. Data augmentation: changing texts to upper or lower case; removing all punctuation, adding dot at the end of a sentence. It was applied because otherwise the model is over reliant on punctuation and capitalization and does not pay enough attention to other features. Loss: binary classification (on GYAFC), in batch ranking (on PT data). Performance metrics on the test data: dataset ROC AUC precision recall fscore accuracy Spearman GYAFC 0.9779 0.90 0.91 0.90 0.9087 0.8233 GYAFC normalized (lowercase + remove punct.) 0.9234 0.85 0.81 0.82 0.8218 0.7294 P&T subset Spearman R news 0.4003 answers 0.7500 blog 0.7334 email 0.7606 Citation If you are using the model in your research, please cite the following paper where it was introduced: Licensing Information [Creative Commons Attribution NonCommercial ShareAlike 4.0 International License][cc by nc sa]. [![CC BY NC SA 4.0][cc by nc sa image]][cc by nc sa] [cc by nc sa]: http://creativecommons.org/licenses/by nc…
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