Comments (2)
Hi everyone, our codes and datasets are ready.
from p-tuning.
Thanks for your interest. We have some additional experiment results to refine and report, and this approximately requires one to two weeks.
from p-tuning.
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。
- P-tuning中模型参数冷冻的问题 HOT 2
- 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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from p-tuning.