Comments (1)
yhcc
commented on September 25, 2024
Yes, when pre-trained with various text, the vanilla Transformer can also have very good sense of direction and distance. And this has been verified by many papers which investigate the effectiveness of BERT. Our proposal wants to discuss why the vanilla transformer cannot do well in the NER task, and based on the discussion we make some improvements, and luckily it worked. But this is by no means the only way to solve this problem. And BERT can definitely achieve better performance than TENER.
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Related Issues (20)
- Testing and predicting with TENER HOT 1
- 关于PE向量的问题，为什么要shift?以及为什么要transpose_shift？是为了计算R中的t-j吗 HOT 2
- For the unigram and bigram embedding for Chinese dataset, what if i wanna train my own set of embedding?? HOT 1
- RelativeMultiHeadAttn
- 一个关于weibo数据集的问题 HOT 2
- Can this be used on non CONLL-2003 data format? HOT 1
- 调取batch模块读取数据时出现的无限递归问题 HOT 1
- attention score计算的疑问 HOT 2
- 如何将中文数据集转为
- Significance of using 'num_embeddings' and 'origin_shift' HOT 1
- how to (1) Prepare the English dataset.? HOT 1
- How to split the msra dataset? HOT 1
- reproducing the results on the paper HOT 1
- 请问测试的代码没有吗 HOT 2
- 请教如何理解函数_transpose_shift HOT 1
- -
- 测试结果 HOT 1
- 训练结果问题 HOT 4
- 论文中取哪一个结果，评价指标问题
- 请问一下用训练好的模型做测试？
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