Comments (1)
Hi, sorry for the late reply. I personally think using BIO or other schemes will also be fine. Using IO scheme is more due to a simplicity consideration (so that tokens are only to be decided if they are part of entity mention or not).
Using BIO scheme means there are more labels and the label distribution may vary more when performing few-shot sampling. So it may require more consideration and analysis in terms of data sampling. We didn't really do experiment on that, maybe you can try that.
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Related Issues (20)
- UnboundLocalError: local variable 'label' referenced before assignment HOT 2
- 您好,想问一下data_split.py和processing.py是干嘛的呢 HOT 1
- Code and dataset license HOT 1
- 您好,请问论文中提到的给few-nerd注释的接口代码是否公开了呢 HOT 4
- 您好,请问fewshot训练的时候加不加--use_sampled_data结果没有区别吧?只是简化训练过程了是嘛 HOT 1
- 您好,我在bash run_supervised.sh的时候出现了如下报错,请问可能是什么原因呢? HOT 2
- 您好,请教一个关于few-shot实验的细节问题。 HOT 1
- 无法下载按照episode采样好的数据集 HOT 1
- Using custom dataset HOT 2
- episode 链接失效了
- Structshot
- Regarding Data-set in inter/intra folder HOT 4
- How to do inference on my custom data after training on FewNERD data? HOT 13
- What is the difference between episode-data model training and non-episode-data. HOT 5
- How to create few shot episode-data for training and test from the general custom NER data?
- 如何使用模型
- I've got ERROR 404:Not Found when I try to download through bash file HOT 2
- download link seems to have expired HOT 1
- Raw dataset links not working HOT 2
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