Model Overview
This is the model presented in the paper "Detecting Text Formality: A Study of Text Classification Approaches".
XLM-Roberta-based classifier trained on XFORMAL -- 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
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.
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