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---
language:
- en
tags:
- formality
datasets:
- GYAFC
- Pavlick-Tetreault-2016
---
The model has been trained to predict for English sentences, whether they are formal or informal.
Base model: `roberta-base`
Datasets: [GYAFC](https://github.com/raosudha89/GYAFC-corpus) from [Rao and Tetreault, 2018](https://aclanthology.org/N18-1012) and [online formality corpus](http://www.seas.upenn.edu/~nlp/resources/formality-corpus.tgz) from [Pavlick and Tetreault, 2016](https://aclanthology.org/Q16-1005).
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
|