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Cross-domain Few-Shot NER Result #17

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lvminghan1997 opened this issue May 4, 2022 · 5 comments
Open

Cross-domain Few-Shot NER Result #17

lvminghan1997 opened this issue May 4, 2022 · 5 comments

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@lvminghan1997
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in no source-domain data,MIT Movie 10 shot ,the result in paper is 37.3,why i get 51.06 , can you tell me some detail about that?

@lplping
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lplping commented May 9, 2022

I have the same doubt, i get 0.496 in MIT Movie 10 shot

@zhanghaok
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I have the same doubt, i get 0.496 in MIT Movie 10 shot

您好!我现在已经在CoNLL03上复现了你的结果,也生成了对应的模型,请问我如何在MIT Movie少样本数据集上进行微调呢,直接在train.py中修改数据集的路径,然后运行就可以了吗?

@zhanghaok
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您好!我现在已经在CoNLL03上复现了你的结果,也生成了对应的模型,请问我如何在MIT Movie少样本数据集上进行微调呢,直接在train.py中修改数据集的路径,然后运行就可以了吗?

@Nealcly
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Nealcly commented May 16, 2022

One possible reason is the sample details. Could you sample multiple times and calculate the average performance?

@bwl666
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bwl666 commented Apr 30, 2023

in no source-domain data,MIT Movie 10 shot ,the result in paper is 37.3,why i get 51.06 , can you tell me some detail about that?

Hello, I would like to ask if dev .txt is not needed in cross-domain experiments such as mit-movie. If not, will this overfit?

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