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liuxinchen3 2c216b9e59 | 1 year ago | |
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Data | 1 year ago | |
dataloaders | 1 year ago | |
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model_save | 1 year ago | |
models | 1 year ago | |
trainers | 1 year ago | |
.DS_Store | 1 year ago | |
README.md | 1 year ago | |
config.py | 1 year ago | |
loggers.py | 1 year ago | |
main.py | 1 year ago | |
options.py | 1 year ago | |
requirements.txt | 1 year ago | |
templates.py | 1 year ago | |
utils.py | 1 year ago |
This repository implements models RoBERTa4Item. Use RoBERTa4Item model to learn the dynamic embedding information of item.
Run main.py
with arguments to train and/or test you model. There are predefined templates for all models.
On running main.py
, it asks you whether to train on MovieLens-1m or MovieLens-20m. (Enter 1 or 20)
python main.py --template train_bert
Pre-tained model placed in ‘model_save’.
This work was supported by the National Key R&D Program of China under Grant No. 2020AAA0103804(Sponsor: Hefu Liu). This work belongs to the University of science and technology of China.
本项目提出了一种学习商品的embedding表示的预训练算法模型RoBERTa4Item,本算法利用RoBERTa算法模型作为backbone,通过RoBERTa算法根据用户与商品的交互信息历史学习商品的动态嵌入信息。
Python Text
Dear OpenI User
Thank you for your continuous support to the Openl Qizhi Community AI Collaboration Platform. In order to protect your usage rights and ensure network security, we updated the Openl Qizhi Community AI Collaboration Platform Usage Agreement in January 2024. The updated agreement specifies that users are prohibited from using intranet penetration tools. After you click "Agree and continue", you can continue to use our services. Thank you for your cooperation and understanding.
For more agreement content, please refer to the《Openl Qizhi Community AI Collaboration Platform Usage Agreement》