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question-difficulty-prediction's Introduction

🍻 Welcome stranger

  • 🎓: A Ph.D graduated from University of Science and Technology of China (USTC).
  • 🎯: Interested in NLP & Education area. Mainly Focusing on Hierarchical Multi-label Classification Tasks.
  • 👾: I'm a die-hard fan of the Fallout ☢️ and Witcher 🐺 series, and I especially love CRPG games & indie games. Feel free to add my Steam.

🧰 My Projects

  • 🛠 Multi-label Classification: a collection of deep neural network models for beginners, designed to solve Multi-label Classification problems, built in TensorFlow.
  • 🛠 Semantic Textual Similarity: a collection of deep neural network models for beginners, designed to solve Semantic Textual Similarity problems, built in TensorFlow.
  • 📚 Question Difficulty Prediction: a collection of deep neural network models for Question Difficulty Prediction problems, built in TensorFlow & PyTorch.
  • 🍻 HARNN: an attention-based recurrent network Approach for Hierarchical Multi-label Text Classification (HMC), accepted at CIKM 2019 and built in TensorFlow. [Papers] | [Code]
  • 🍻 HmcNet: a general approach that incorporates both explicit and implicit class hierarchy constraints, designed to solve Hierarchical Multi-label Text Classification (HMC), accepted at TKDE 2022 and built in PyTorch. [Papers] | [Code]
  • 🍻 HMNet: a multi-modal method that captures class dependencies to solve Educational Video Concept Prediction problems, accepted at JMLC 2023. [Papers]

🎉 News

  • [2023 Aug] Gods Among Us, Baldur's Gate 3!
  • [2023 Jun] I've suffered in the Darkest Dungeon, and no one is spared!
  • [2023 May] I've been engrossed in the The Legend of Zelda: Tears of the Kingdom, Nintendo rules the fxxking world!
  • [2023 Apr] I've cleared Octopath Traveler II, スクエニさん、さすがかよ!
:octocat: All Works

:octocat: 2023

:octocat: 2022

:octocat: 2021

:octocat: 2020

:octocat: 2019

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question-difficulty-prediction's Issues

缺少数据集

Test_pairwise_sample.json该文件不存在,将param_parser.py中Test_pairwise_sample.json替换为Test_sample.json后,运行C-MIDP中的train.py文件,报错,提示utils.py文件中 data_word2vec(input_file, word2vec_model)函数报错,能提供一下Test_pairwise_sample.json文件吗

MIDP模型与数据问题

您好,
我自己也尝试搭建过C/R/H-MIDP模型,训练数学科目的数据,在没有context时,模型的PCC能达到60%,但是使用context时却只能达到55%,我的context定义是同一张试卷,同一个学校。
我看了您的MIDP模型(Pytorch版)的代码,但是在数据处理部分,并没有pairwise数据的生成代码,请问是否遗漏了,而且在运行您的C-MIDP(Pytorch版)的代码时,出现了UnicodeDecodeError的问题。
我进行模型评估时是使用context分组计算PCC的,与您的代码中不同,请问论文《数据驱动的数学试题难度预测》中的模型评估是否需要分组计算PCC?

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