Comments (6)
@batselem
Hi, thank you for reading the code, link_prediction gives the rank of every enity in correct (positive) test triples, so the labels are "1".
from kg-bert.
Thank you very much for a quick response!
Yes but tail_corrupt_list contains corrupted triples. How did you label them? As I skim through it, it looks like all the triples in tail_corrupt_list get "1" label.
Thank you.
from kg-bert.
Ah, even though they all get label "1", you only get the rank of the first triple which is the original correct triple: rank1 = np.where(argsort1 == 0)[0][0]
Am I correct? Thanks.
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@batselem
Yes, all the triples in tail_corrupt_list get "1" label. This is inappropriate when writing the code,but only the "1" of the target postive tiple is used to obtain values (probability of being correct) of the target postive tiple and corrputed tiples.
# get the dimension corresponding to current label 1 of all corrupted triples and the positive test triple
rel_values = preds[:, all_label_ids[0]]
Here all_label_ids[0] is the label of the target positive triple.
The "1" of corrupted triples are not used in any computation, and the ranks should be correct.
from kg-bert.
I understand it, Thank you very much. Excellent work!
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@batselem
Thank you!
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Related Issues (20)
- epoch not working, pytorch issue? HOT 1
- list index out of error HOT 3
- estimated prediction time? HOT 1
- 是否支持中文 HOT 2
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- 请问该文发表到正式期刊或会议了吗? HOT 2
- why umls dataset does not use long text? HOT 1
- 关于FB15K-237数据集文本句子的问题 HOT 2
- Run the model over a different kg
- 作者你好 请问下您的这篇论文现在发布在哪篇期刊上了? HOT 2
- 作者您好,我在您的实验的基础上将BERT模型替换为了DistilBERT模型,运行fb15k-237数据集,但是ACC为0,您知道是什么原因嘛
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