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我有两个问题: 1.为什么在中文数据集的效果上,utc-large还比不上uct-base,我测试了三个数据集,两个开放数据集和一个自己的数据集,基本上都是这样的结论。utc-large和utc-base应该是只有训练的语料库不一样,utc-large参数量越大,反而效果越差。 2.utc-base和utc-large的基座模型是erine3.0嘛,按照usm的论文描述,训练格式和微调格式是一样的,那是不是可以认为预训练和微调的代码是都是run_train.py,只不过是基座模型不一样。如果是这样的话,想问问erine3.0到utc中使用了哪些中文的分类数据集呢?
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同问
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效果不好的原因可能与学习率等有关,模型越大越容易过拟合,需要的数据就越多。
另外,目前没有开放utc-base中文模型训练细节的计划。
wawltor
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我有两个问题:
1.为什么在中文数据集的效果上,utc-large还比不上uct-base,我测试了三个数据集,两个开放数据集和一个自己的数据集,基本上都是这样的结论。utc-large和utc-base应该是只有训练的语料库不一样,utc-large参数量越大,反而效果越差。
2.utc-base和utc-large的基座模型是erine3.0嘛,按照usm的论文描述,训练格式和微调格式是一样的,那是不是可以认为预训练和微调的代码是都是run_train.py,只不过是基座模型不一样。如果是这样的话,想问问erine3.0到utc中使用了哪些中文的分类数据集呢?
The text was updated successfully, but these errors were encountered: