---
language:
- en
- fr
- it
- pt
tags:
- formal or informal classification
licenses:
- cc-by-nc-sa
license: openrail++
base_model:
- FacebookAI/xlm-roberta-base
---

**Model Overview**

This is the model presented in the paper ["Detecting Text Formality: A Study of Text Classification Approaches"](https://aclanthology.org/2023.ranlp-1.31/). 

XLM-Roberta-based classifier trained on [XFORMAL](https://aclanthology.org/2021.naacl-main.256.bib) -- a multilingual formality classification dataset.


**Results**
All languages

|              | precision | recall   | f1-score | support |
|--------------|-----------|----------|----------|---------|
| 0            | 0.744912  | 0.927790 | 0.826354 | 108019  |
| 1            | 0.889088  | 0.645630 | 0.748048 | 96845   |
| accuracy     |           |          | 0.794405 | 204864  |
| macro avg    | 0.817000  | 0.786710 | 0.787201 | 204864  |
| weighted avg | 0.813068  | 0.794405 | 0.789337 | 204864  |


EN

|              | precision | recall   | f1-score | support |
|--------------|-----------|----------|----------|---------|
| 0            | 0.800053  | 0.962981 | 0.873988 | 22151   |
| 1            | 0.945106  | 0.725899 | 0.821124 | 19449   |
| accuracy     |           |          | 0.852139 | 41600   |
| macro avg    | 0.872579  | 0.844440 | 0.847556 | 41600   |
| weighted avg | 0.867869  | 0.852139 | 0.849273 | 41600   |

FR

|              | precision | recall   | f1-score | support |
|--------------|-----------|----------|----------|---------|
| 0            | 0.746709  | 0.925738 | 0.826641 | 21505   |
| 1            | 0.887305  | 0.650592 | 0.750731 | 19327   |
| accuracy     |           |          | 0.795504 | 40832   |
| macro avg    | 0.817007  | 0.788165 | 0.788686 | 40832   |
| weighted avg | 0.813257  | 0.795504 | 0.790711 | 40832   |

IT

|              | precision | recall   | f1-score | support |
|--------------|-----------|----------|----------|---------|
| 0            | 0.721282  | 0.914669 | 0.806545 | 21528   |
| 1            | 0.864887  | 0.607135 | 0.713445 | 19368   |
| accuracy     |           |          | 0.769024 | 40896   |
| macro avg    | 0.793084  | 0.760902 | 0.759995 | 40896   |
| weighted avg | 0.789292  | 0.769024 | 0.762454 | 40896   |

PT

|              | precision | recall   | f1-score | support |
|--------------|-----------|----------|----------|---------|
| 0            | 0.717546  | 0.908167 | 0.801681 | 21637   |
| 1            | 0.853628  | 0.599700 | 0.704481 | 19323   |
| accuracy     |           |          | 0.762646 | 40960   |
| macro avg    | 0.785587  | 0.753933 | 0.753081 | 40960   |
| weighted avg | 0.781743  | 0.762646 | 0.755826 | 40960   |


## How to use
```python
from transformers import XLMRobertaTokenizerFast, XLMRobertaForSequenceClassification

# load tokenizer and model weights
tokenizer = XLMRobertaTokenizerFast.from_pretrained('s-nlp/xlmr_formality_classifier')
model = XLMRobertaForSequenceClassification.from_pretrained('s-nlp/xlmr_formality_classifier')

id2formality = {0: "formal", 1: "informal"}
texts = [
    "I like you. I love you",
    "Hey, what's up?",
    "Siema, co porabiasz?",
    "I feel deep regret and sadness about the situation in international politics.",
]

# prepare the input
encoding = tokenizer(
    texts,
    add_special_tokens=True,
    return_token_type_ids=True,
    truncation=True,
    padding="max_length",
    return_tensors="pt",
)

# inference
output = model(**encoding)

formality_scores = [
    {id2formality[idx]: score for idx, score in enumerate(text_scores.tolist())}
    for text_scores in output.logits.softmax(dim=1)
]
formality_scores
```

```
[{'formal': 0.993225634098053, 'informal': 0.006774314679205418},
 {'formal': 0.8807966113090515, 'informal': 0.1192033663392067},
 {'formal': 0.936184287071228, 'informal': 0.06381577253341675},
 {'formal': 0.9986615180969238, 'informal': 0.0013385231141000986}]
```

## 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.