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View Code? Open in Web Editor NEWSource code of CIKM 2022 and DLP-KDD workshop 2022 Best Paper: IntTower-“ IntTower: the Next Generation of Two-Tower Model for Pre-ranking System”
License: Apache License 2.0
Source code of CIKM 2022 and DLP-KDD workshop 2022 Best Paper: IntTower-“ IntTower: the Next Generation of Two-Tower Model for Pre-ranking System”
License: Apache License 2.0
Can you please post the download link for the Alibaba dataset? I can not find it in the link
RuntimeError: CUDA out of memory. Tried to allocate 32.00 MiB (GPU 0; 23.99 GiB total capacity; 37.27 GiB already allocated; 0 bytes free; 37.33 GiB reserved in total by PyTorch)
I run this code on 24G GPU, this error always happened after epoch 2 whatever batch_size I set, is there anything wrong with my environment?
Thank you for your great job. Could you provide the training script for the dataset of Amazon and Alibaba? We want to know more detail about your great job for following. Thank you very much!
Hello!
First of all thanks a lot for your great article and for opening the code base.
I have a question regarding the model serving:
I understand that you create Faiss indices based on the multi-head latent representation of the items but how do you query them? Do you use the multi-head latent representation of the last layer of the user tower? And after retrieving the top K items, do you compute the Fe score to rerank the candidates?
有个小问题想请假下哈
Lines 175 to 178 in a234d89
2.contrastive loss 这个地方为啥用y 去作为索引 选择cos_sim score呢, 比如batch_size 256, 那岂不是都选到前两个的score了 后面254个的都选不到, 另外一般这种不是只包含正例 这里面应该是正负的label 都有?
IntTower/preprocessing/utils.py
Lines 74 to 76 in a234d89
I have several questions:
Thanks!
Thanks for your great job! I wonder if you can provide the example code on how to deploy the IntTower in real scenario? such as how to execute the multi-head faiss and maxsim in a parallel way.
您好,请教一下,如果我的粗排有多个目标,比如,ctr,cvr, 想问一下在预测时如何进行融合,目前我想到的,
1、 使用multi-head分别对ctr塔和cvr塔的顶部进行提取,将提取得到的ctr embedding以及cvr embedding分别和multi-head提取的user embedding过一遍fe_score函数,然后将 ctr的fe_score和cvr的fe_score 以一定权重进行融合,得到最终的score
2、使用multi-head分别对ctr塔和cvr塔的顶部进行提取,将提取得到的ctr embedding以及cvr embedding以一定权重进行融合,将融合后的embedding 和multi-head提取的user embedding过一遍fe_score函数,得到最终的score
谢谢~
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