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---
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 |