Comments (2)
Deerkangkang
commented on May 20, 2024
我理解意义上也是需要对整体模型进行微调
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qiuwenbogdut
commented on May 20, 2024
您好，我理解的p-tuning的原理是冷冻后面大语言模型，只调整前面的prompt embedding模型。但是在您代码的实现中(https://github.com/THUDM/P-tuning/blob/main/PT-Fewshot/pet/wrapper.py 中optimizer部分) 同时对后面大语言模型的参数进行了微调，想问下这部分是我理解错了吗
可以参考一下之前的一个issue #4
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Related Issues (20)
- fewGLUE中采用的template format
- Inconsistent SuperGLUE Results from P-Tuning and P-TuningV2 Paper HOT 2
- P-tuning如何用于Question answering HOT 1
- 关于p-tuning中训练loss变为nan HOT 1
- loss, logits = output.loss, output.logits AttributeError: 'tuple' object has no attribute 'loss' HOT 1
- lstm的enbedding_dim与预训练模型的embedding_dim不一致的报错？ HOT 7
- Tuning joe
- questions about discreteness in optimization HOT 2
- Doesn't anyone see a problem with the code about the prompt construction for BERT-style transformer??? HOT 2
- fully-supervised learning那部分的实验的具体细节。
- why does LSTM can be discarded during inference? HOT 1
- AttributeError: 'Namespace' object has no attribute '_get_node_flag' HOT 1
- The link to LAMA dataset is broken HOT 1
- MASK and x_h reversed on bert
- Why discreteness of word embedding leads to the optimizer easily fall into local minima? HOT 1
- LAMA数据集链接已经失效 HOT 1
- python cli.py AttributeError: 'Namespace' object has no attribute '_get_node_flag'
- LAMA link is bad HOT 1
- which paper is the first one to propose soft prompt HOT 1
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