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View Code? Open in Web Editor NEWAdversarial Transfer Learning for Chinese Named Entity Recognition with Self-Attention Mechanism
Adversarial Transfer Learning for Chinese Named Entity Recognition with Self-Attention Mechanism
按照您设置的最大长度80,我对三个数据集进行统计,
训练集,验证集,测试集,句子总数为:135 270 270
大于80的句子数占各个数据集的比例为:0.22,0.20,0.22,跟您前面说过的小于80的占95%以上不符,也就是你说的这个比例是小于0.05,这个差距有点大,谢谢。
Hi, dear PengFei Cao, I have try my best to get the SigHan Dataset, But still not find. I have send a email to you for help. So if you can, can you share the dataset with me?
用了作者训练的模型,后来又用自己训练的模型测试,但测试结果都很差,请问test_weibo.py是否完整
Excuse me .where can I get the paper? Or can you send the paper to my email?[email protected]
thx
I release a pytorch version about this model in https://github.com/tyistyler/pytorch-AT4ChineseNER, maybe it has some questions and we can talk about it~
data目录下的msr_training.utf8怎么没有?
Can I ask where to find this paper?
Hi there,
As SIGHANNER dataset isn't available on your given link, if other MSRA datasets are used it shows following errors.
GPU:
InvalidArgumentError (see above for traceback): indices[0,1] = 59 is not in [0, 49)
[[node ner_model/ner_crf/GatherV2_1 (defined at <ipython-input-2-f9f661903dfb>:176) ]]
CPU:
InvalidArgumentError (see above for traceback): indices[0,0] = 59 is not in [0, 49)
[[Node: ner_model/ner_crf/Gather_1 = Gather[Tindices=DT_INT32, Tparams=DT_FLOAT,
validate_indices=true, _device="/job:localhost/replica:0/task:0/cpu:0"]
(ner_model/ner_crf/Reshape_3, ner_model/ner_crf/add_3)]]
我使用这个代码发现在测试集上最好的F1:51.51,达不到53.08。
[email protected]
thanks
In your published paper, you claim that "Since the SighanNER does not have development set, we sample 10% data of training set as development set". However, I can neither find development set of SighanNER ,or development set of MSRANER that used for selecting best model in your code.
In my NER experiments, it seems that self-attention did not improve the model's effectiveness.
I just had a problem. I want to confirm it.
有谁能分享一下这个数据集吗
好像adv_loss是在训练一个数据集来自那个任务的分类器,那么这个分类器是如何提高命名实体的精度的呢,菜鸟求解答
预训练中的embed.txt是怎么得来的呢?是通过word2vec得来的,还是随机生成的?
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