利用《变形金刚》的双向编码器表征识别印尼社交媒体上的厌女症(BERT)

Bagas Tri Wibowo, Dade Nurjanah, Hani Nurrahmi
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引用次数: 0

摘要

厌女症是一种厌恶或不喜欢女性的行为。文本分类可以用来识别厌女症文本。目前比较流行的一种文本分类方法是双向编码器从变压器(BERT)。微调是一种将知识从已训练好的模型转移到新模型以完成新任务的方法。本研究主要利用IndoNLU提供的IndoBert预训练模型构建厌女症识别模型。Misogyny模型的识别准确率最高,达到83.74%,K-fold交叉验证的平均验证值为77.86%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification of Misogyny on Social Media in Indonesian Using Bidirectional Encoder Representations From Transformers (BERT)
Misogyny is a behavior that hates or dislikes women Text classification can be used to identify misogyny text. One text classification method currently popular and proven to have good performance is the Bidirectional Encoder From Transformers (BERT). Fine-tuning is a method to transfer knowledge from a trained model to a new model to complete a new task. This study focuses on building a misogyny identification model with IndoBert pre-trained model provided by IndoNLU. The identification of Misogyny model obtained the best results with an accuracy value of 83.74% and by using K-fold cross-validation, the average validation value is 77.86%.
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