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---
language:
- en
tags:
- formality
licenses:
- cc-by-nc-sa
---
**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.
| | 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**
```
TBD
```
## 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 |