Russian Sensitive Topics

🚀 俄語敏感話題分類模型

本模型用於對俄語敏感話題進行分類，可預測18種敏感話題的組合。它基於擴展的俄語敏感話題數據集進行訓練，能助力檢測可能損害公司聲譽的不當信息。

🚀 快速開始

該模型可預測文章中描述的18種敏感話題的組合。你可以在此處找到使用該模型的詳細步驟說明。

✨ 主要特性

• 針對性訓練：在俄語敏感話題數據集上進行訓練，能精準識別特定敏感話題。
• 多話題預測：可預測18種敏感話題的組合，滿足多樣化檢測需求。

📚 詳細文檔

模型概述

此模型在俄語敏感話題數據集上進行訓練。敏感話題的概念在這篇文章中有詳細描述，該文章於EACL - 2021會議的波羅的海 - 斯拉夫語自然語言處理研討會上發表。需注意，文章描述的是數據集的第一版，而模型是基於擴展版本的數據集進行訓練的，該擴展數據集已在我們的GitHub或kaggle上開源。數據集的特性與文章中描述的一致，僅規模有所不同。

評估指標

數據集部分樣本為手動標註，部分為半自動標註，更多信息可查看我們的文章。我們僅在手動標註的數據部分測試了分類器的性能，因此部分話題在測試集中的代表性不足。

📄 許可證

本項目採用知識共享署名 - 非商業性使用 - 相同方式共享 4.0 國際許可協議。

🔖 引用

如果您覺得這個倉庫有幫助，請引用我們的論文：

@inproceedings{babakov-etal-2021-detecting,
    title = "Detecting Inappropriate Messages on Sensitive Topics that Could Harm a Company{'}s Reputation",
    author = "Babakov, Nikolay  and
      Logacheva, Varvara  and
      Kozlova, Olga  and
      Semenov, Nikita  and
      Panchenko, Alexander",
    booktitle = "Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing",
    month = apr,
    year = "2021",
    address = "Kiyv, Ukraine",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2021.bsnlp-1.4",
    pages = "26--36",
    abstract = "Not all topics are equally {``}flammable{''} in terms of toxicity: a calm discussion of turtles or fishing less often fuels inappropriate toxic dialogues than a discussion of politics or sexual minorities. We define a set of sensitive topics that can yield inappropriate and toxic messages and describe the methodology of collecting and labelling a dataset for appropriateness. While toxicity in user-generated data is well-studied, we aim at defining a more fine-grained notion of inappropriateness. The core of inappropriateness is that it can harm the reputation of a speaker. This is different from toxicity in two respects: (i) inappropriateness is topic-related, and (ii) inappropriate message is not toxic but still unacceptable. We collect and release two datasets for Russian: a topic-labelled dataset and an appropriateness-labelled dataset. We also release pre-trained classification models trained on this data.",
}