Comments (5)
But it seems that if I want to test that model I need to reduce the number significantly… I 'll try to run paper writing, anyway thanks again for your effort.
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Hi @bhcsayx , thank you very much for your interest in our research! I think your phenomenon is normal. Actually our code ran about two weeks to get the result. The reason for the slower running time is that for each head, the model will iterate over every tail and every relation to check whether a valid triple exists. Therefore, time complexity is very high.
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Feel free to ask me if you have any further questions. @bhcsayx
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Thank you! Maybe I should consider getting access to some better GPU to run it…
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Maybe you can reduce the test sample a little bit or run my paper writing directly.
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Related Issues (20)
- How 'terms' are related to KB HOT 6
- excellent completed work !! can you share some details about how to produce the dataset,such as entity_text_title_tokenized.json,term.pth in PubMed Paper Reading Dataset ? thanks HOT 36
- 处理one-hop节点的代码的一个疑问 HOT 4
- It said that I need to have more RAM?tks HOT 12
- How many epoches do you use in these two tasks? HOT 3
- Training part of the dataset HOT 2
- --
- Model weights sharing and training stopping criterion HOT 3
- Something has been deprecated. HOT 1
- How to generate KGs? HOT 4
- Questions about code and paper_reading dataset HOT 2
- 这个项目现在还更新吗,还有人在使用吗?
- PubTator-MeSH-CTD HOT 1
- METEOR in eval.py HOT 4
- CUDA out of memory HOT 7
- Are new entities formed or just links? HOT 2
- RuntimeError HOT 13
- TypeError: can't convert np.ndarray of type numpy.int32. HOT 18
- TypeError: can't convert np.ndarray of type numpy.int32. HOT 3
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