Xlmr Formality Classifier

🚀 多言語形式分類モデル

このモデルは、テキストの形式分類に特化した多言語対応モデルです。論文の研究成果を基に構築され、多言語のテキストに対して形式的か非形式的かを高精度に分類できます。

🚀 クイックスタート

本モデルは、論文 "Detecting Text Formality: A Study of Text Classification Approaches" で提示されたものです。XLM-Robertaベースの分類器で、XFORMAL という多言語形式分類データセットで学習されています。

モデル情報

結果

全言語

英語 (EN)

フランス語 (FR)

イタリア語 (IT)

ポルトガル語 (PT)

💻 使用例

基本的な使用法

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)
]
print(formality_scores)

出力例:

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

📚 引用情報

@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.",
}

📄 ライセンス

このモデルはOpenRAIL++ライセンスの下で提供されています。このライセンスは、産業界と学界の両方における様々な技術開発を支援し、公共の利益に役立つことを目的としています。