Comments (1)
Thank you for your attention and sorry for the late reply.
We have released the code and script for training and testing.
Take the End2end MoRec for text recommendation (Bert-base + SASRec) as an example, you can run 'cd bce_text/main-end2end', then 'python train_bert_base.py'.
The files looked like 'train_xxx.py' are the scripts, you can change the parameters in these files, the details maybe need to be adjusted based on your GPU resources and Python environment.
It is worth noting that "LLMs" usually refer to models with comparable parameters to GPT-3, much larger than the Bert and Roberta used in our paper. Due to the GPU limitation and the challenges of fine-tuning, LLMs are not considered in our paper. How to apply End2end LLMs to Recsys is a valuable research direction.
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Related Issues (9)
- Where is the DSSM code HOT 2
- 验证速度很慢
- 可否提供训练好的模型文件 HOT 1
- Training problem about local_rank
- Hyperparameter about best performance. HOT 2
- 测试时为啥候选物品的embedding 不需要过Model模块的fc呢,也即MLP模块,而是直接使用物品的modalembedding或者说id embedding HOT 2
- NDCG10以及Hit10的计算是不是又问题,我跑了下modal模式,很多都大于1,甚至到6,7.太诡异了。 HOT 1
- About In-batch debiased cross-entropy loss HOT 2
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