--- language: - en tags: - formality licenses: - cc-by-nc-sa license: cc-by-nc-sa-4.0 --- **Model Overview** This is the model presented in the paper "Detecting Text Formality: A Study of Text Classification Approaches". The original model is [DeBERTa (large)](https://huggingface.co/microsoft/deberta-v3-large). Then, it was fine-tuned on the English corpus for fomality classiication [GYAFC](https://arxiv.org/abs/1803.06535). In our experiments, the model showed the best results within Transformer-based models for the task. More details, code and data can be found [here](https://github.com/s-nlp/formality). **Evaluation Results** Here, we provide several metrics of the best models from each category participated in the comparison to understand the ranks of values. This is the task of English monolingual formality classification. | | acc | f1-formal | f1-informal | |------------------|------|-----------|-------------| | bag-of-words | 79.1 | 81.8 | 75.6 | | CharBiLSTM | 87.0 | 89.0 | 84.0 | | DistilBERT-cased | 80.1 | 83.0 | 75.6 | | DeBERTa-large | 87.8 | 89.0 | 86.1 | **How to use** ```python from transformers import AutoModelForSequenceClassification, AutoTokenizer model_name = 'deberta-large-formality-ranker' tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained(model_name) ``` **Citation** ``` @inproceedings{dementieva-etal-2023-detecting, title = "Detecting Text Formality: A Study of Text Classification Approaches", author = "Dementieva, Daryna and Babakov, Nikolay and Panchenko, Alexander", editor = "Mitkov, Ruslan and Angelova, Galia", booktitle = "Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing", month = sep, year = "2023", address = "Varna, Bulgaria", publisher = "INCOMA Ltd., Shoumen, Bulgaria", url = "https://aclanthology.org/2023.ranlp-1.31", pages = "274--284", abstract = "Formality is one of the important characteristics of text documents. The automatic detection of the formality level of a text is potentially beneficial for various natural language processing tasks. Before, two large-scale datasets were introduced for multiple languages featuring formality annotation{---}GYAFC and X-FORMAL. However, they were primarily used for the training of style transfer models. At the same time, the detection of text formality on its own may also be a useful application. This work proposes the first to our knowledge systematic study of formality detection methods based on statistical, neural-based, and Transformer-based machine learning methods and delivers the best-performing models for public usage. We conducted three types of experiments {--} monolingual, multilingual, and cross-lingual. The study shows the overcome of Char BiLSTM model over Transformer-based ones for the monolingual and multilingual formality classification task, while Transformer-based classifiers are more stable to cross-lingual knowledge transfer.", } ``` ## 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-sa/4.0/ [cc-by-nc-sa-image]: https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png