Comments (4)
Same here, got the result of 77.33.
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Me too.
from diffcse.
Here also :(
Below is my result, with batch size and etc. same as written in paper.
from diffcse.
Hi all!
After I tried some experiments on another machine, I found that the hyperparams are very sensitive to the device you use. I cannot reproduce the results on another machine with the same hyperparams either. Your python/pytorch/cuda/huggingface version will affect your results. The hyperparams on the paper are only suitable for the first machine I used, so you probably need to re-search hyperparams to get the same results on your machine.
A recent CSE paper https://github.com/yiren-jian/NonLing-CSE/tree/main/VisualCSE seems also have this problem for CSE. They suggest use the same hardwares and software versions to faithfully reproduce the results in the paper.
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Related Issues (20)
- Implementing Error HOT 1
- Command to replicate transfer results
- Question about scalability wrt. input length HOT 2
- Where is the code of generator(fixed)?
- grid serch
- 第六章 Retrieval Task 的原始碼是否有放在 Github 呢? HOT 2
- RuntimeError: mat1 dim 1 must match mat2 dim 0 HOT 2
- alignment & uniformity 原始碼與訓練細節
- when will the parameter 'neg_sim' be 'nan'?
- How to install? HOT 5
- Index out of range in self
- Discrepancies in DiffSCE Code Execution and Reported Results: Seeking Insight
- “Why don’t you have a code file to convert the model format to Hugging Face format? Can your model be evaluated directly after being saved without conversion?
- close
- AttributeError: module 'dill._dill' has no attribute 'stack' HOT 1
- torch.nn.modules.module.ModuleAttributeError: 'DataParallel' object has no attribute 'sim' HOT 3
- RuntimeError: Input tensor at index 3 has invalid shape [14, 14], but expected [14, 17] HOT 2
- What is the use of lm_head?
- 无法加载离线数据集
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