自动化论文评分-使用LSTM:一种深度学习模型,可预测给定输入文章的分数-源码

上传者: 42104181 | 上传时间: 2021-06-27 09:32:40 | 文件大小: 34.95MB | 文件类型: ZIP
自动化论文评分-使用LSTM 深度学习模型,可预测给定输入文章的分数。 数据集来自惠普基金会提供的Kaggle ASAP竞赛。 如果您要进行交互式演示,则mysite文件夹包含Django应用。 性能准确性由二次加权Kappa(QWK)计算,该度量用于衡量两个评估者之间的一致性。 六年前,这项竞赛的执行水平达到了0.82的QWK分数。 我的模型的QWK得分为0.961。 该模型体系结构由2个长期短期记忆(LSTM)层和一个密集输出层组成。 最后一层使用Relu激活功能。 QWK是通过使用5折交叉验证对数据集进行训练的模型计算得出的,并取所有5折的平均值。

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