This project is mainly based on jeykigung's work access their github repo through P5 repo
Paper link: https://arxiv.org/pdf/2203.13366.pdf
This project takes shashankrajput's commit in jeykigung/P5#3. This repo is exactly same as P5 repo expcept that the data has shuffled item ids.
- Python 3.9.7
- PyTorch 1.10.1
- transformers 4.2.1
- tqdm
- numpy
- sentencepiece
- pyyaml
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Clone this repo
git clone https://github.com/menglin0320/P5-shuffled.git -
Download preprocessed data from this Google Drive link, (The data in this link is shuffled beauty data, I didn't try to make sure the zero shot split still works since I'm not testing those tasks.) then put them into the data folder. If you would like to preprocess your own data, please follow the jupyter notebooks in the preprocess folder. Raw data can be downloaded from this Google Drive link, then put them into the raw_data folder.
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Download pretrained checkpoints into snap folder. If you would like to train your own P5 models, snap folder will also be used to store P5 checkpoints.
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Pretrain with scripts in scripts folder, such as
bash scripts/pretrain_P5_base_beauty.sh 4Here 4 means using 4 GPUs to conduct parallel pretraining.
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Evaluate with example jupyter notebooks in the notebooks folder. Before testing, create a soft link of data folder to the notebooks folder by
cd notebooks ln -s ../data .
See CHECKPOINTS.md.
You can also explore P5 in Hugging Face Hub (https://huggingface.co/makitanikaze/P5).
Please cite the following paper corresponding to the repository:
@inproceedings{geng2022recommendation,
title={Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt \& Predict Paradigm (P5)},
author={Geng, Shijie and Liu, Shuchang and Fu, Zuohui and Ge, Yingqiang and Zhang, Yongfeng},
booktitle={Proceedings of the Sixteenth ACM Conference on Recommender Systems},
year={2022}
}