Deoffxlmr Mono Kannada

🚀 卡納達語混合代碼冒犯性內容檢測模型

本模型用於檢測卡納達語混合代碼語言中的冒犯性內容。名稱中的“mono”指單語設置，即該模型僅使用卡納達語（純語言和混合代碼）數據進行訓練。模型權重初始化為預訓練的XLM - Roberta - Base，並在使用交叉熵損失進行微調之前，在目標數據集上通過掩碼語言建模進行預訓練。

該模型是為EACL 2021達羅毗荼語系語言冒犯性語言識別共享任務訓練的多個模型中表現最優的。基於遺傳算法的集成測試預測在排行榜上獲得了第二高的加權F1分數（保留測試集上的加權F1分數：本模型 - 0.73，集成模型 - 0.74）。

📚 詳細文檔

關於我們的論文詳情

Debjoy Saha、Naman Paharia、Debajit Chakraborty、Punyajoy Saha、Animesh Mukherjee發表了論文“[Hate - Alert@DravidianLangTech - EACL2021: Ensembling strategies for Transformer - based Offensive language Detection](https://www.aclweb.org/anthology/2021.dravidianlangtech - 1.38/)”。

⚠️ 重要提示

請在任何使用這些資源的已發表作品中引用我們的論文。

論文引用格式

@inproceedings{saha-etal-2021-hate,
    title = "Hate-Alert@{D}ravidian{L}ang{T}ech-{EACL}2021: Ensembling strategies for Transformer-based Offensive language Detection",
    author = "Saha, Debjoy and Paharia, Naman and Chakraborty, Debajit and Saha, Punyajoy and Mukherjee, Animesh",
    booktitle = "Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages",
    month = apr,
    year = "2021",
    address = "Kyiv",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2021.dravidianlangtech-1.38",
    pages = "270--276",
    abstract = "Social media often acts as breeding grounds for different forms of offensive content. For low resource languages like Tamil, the situation is more complex due to the poor performance of multilingual or language-specific models and lack of proper benchmark datasets. Based on this shared task {``}Offensive Language Identification in Dravidian Languages{''} at EACL 2021; we present an exhaustive exploration of different transformer models, We also provide a genetic algorithm technique for ensembling different models. Our ensembled models trained separately for each language secured the first position in Tamil, the second position in Kannada, and the first position in Malayalam sub-tasks. The models and codes are provided.",
}

📄 許可證

本項目採用Apache - 2.0許可證。