Train set/test set: We would like to point out that most prior work in unsupervised classification use both the train and test set during training. We believe this is bad practice and therefore propose to only train on the train set. The final numbers should be reported on the test set (see table 3 of our paper). This also allows us to directly compare with supervised and semi-supervised methods in the literature. We encourage future work to do the same. We observe around 2% improvement over the reported numbers when including the test set. - Reproducibility: We noticed that prior work is very initialization sensitive. So, we don't think reporting a single number is therefore fair. We report our results as the mean and standard deviation over 10 runs. Please follow the instructions underneath to perform semantic clustering with SCAN.这段话是什么意思

这段话是一份指南，主要介绍了使用SCAN进行语义聚类的一些注意事项和建议。其中提到了一个关于训练集和测试集的问题：建议只在训练集上进行训练，并在测试集上报告最终结果，以便与其他基于监督和半监督方法进行比较。同时，还建议报告多次运行的平均值和标准差，以提高实验的可重复性。此外，该段落还指出以前的工作很依赖于初始化，因此建议进行多次运行以获得更准确的结果。最后，该段落给出了使用SCAN进行语义聚类的具体操作指南。

相关问题

Train set/test set: We would like to point out that most prior work in unsupervised classification use both the train and test set during training. We believe this is bad practice and therefore propose to only train on the train set. The final numbers should be reported on the test set (see table 3 of our paper). This also allows us to directly compare with supervised and semi-supervised methods in the literature. We encourage future work to do the same. We observe around 2% improvement over the reported numbers when including the test set. Reproducibility: We noticed that prior work is very initialization sensitive. So, we don't think reporting a single number is therefore fair. We report our results as the mean and standard deviation over 10 runs. Please follow the instructions underneath to perform semantic clustering with SCAN.，这段话是什么意思

这段话是在介绍一个名为SCAN的无监督分类方法的实验设置和结果报告。其中，作者指出之前的一些无监督分类方法在训练时同时使用了训练集和测试集，这种做法是不好的，因为会导致模型泛化能力的过度估计。因此，作者建议只在训练集上进行训练，并在测试集上报告最终结果，以便与其他监督和半监督方法进行比较。作者还观察到，之前的一些方法非常依赖于初始化，因此为了保证结果的可重复性，作者进行了10次实验，并报告了平均值和标准差。最后，作者提供了使用SCAN进行语义聚类的指南。

RuntimeError: ./data/Prior.ckpt is a zip archive (did you mean to use torch.jit.load()?)

这个错误通常发生在使用 PyTorch 加载预训练模型时，当您尝试使用 `torch.load()` 加载一个被压缩的 Checkpoint 文件时会出现此错误。要解决这个问题，您可以使用以下两种方法之一：

1. 使用 `torch.jit.load()` 加载模型，如下所示：

import torch.jit

model = torch.jit.load('./data/Prior.ckpt')

2. 将 Checkpoint 文件解压缩并使用 `torch.load()` 加载模型，如下所示：

import zipfile
import torch

# 解压缩 Checkpoint 文件
with zipfile.ZipFile('./data/Prior.ckpt', 'r') as zip_ref:
    zip_ref.extractall('./data/')

# 加载模型
model = torch.load('./data/Prior.ckpt')

注意，如果您选择使用第二种方法，请确保解压缩后的文件名与原始文件名相同，并且解压缩后的文件位于正确的路径中。