s-nlp/deberta-large-formality-ranker

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). Then, it was fine-tuned on the English corpus for fomality classiication GYAFC. 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.

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

from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_name = 's-nlp/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

This model is licensed under the OpenRAIL++ License, which supports the development of various technologies—both industrial and academic—that serve the public good.