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Updated: May 23, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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基于位置-上下文增材变压器模型,用于对社交媒体上的文本数据进行分类

M M Abd-Elaziz1, Nora El-Rashidy2, Ahmed Abou Elfetouh3

  • 1Information Systems Department, Faculty of Computers and Information Sciences, Mansoura University, Mansoura, Egypt. moh_abdelaziz7@hotmail.com.

Scientific reports
|March 8, 2025
PubMed
概括

一个新的位置-上下文添加式变压器模型 (PCA模型) 提高了社交媒体文本分类的准确性. 该PCA模型改进了词嵌入和注意力机制,优于现有模型,并通过更多的训练数据显示了更高的准确性.

关键词:
增加注意力增加注意力.在Bi-LSTM网络中,社交媒体 社交媒体基于变压器的模型字体嵌入 字体嵌入.

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