Comments (2)
ibalazevic
commented on August 27, 2024
We train and test the model using 1-N scoring, the same way as in Dettmers et al., 2018 (also see here): for each relation, we create an inverse by appending "_reverse" to the relation name. That way we are able to train and test only in one direction (desirable due to 1-N scoring), predicting both head and tail, because our dataset doesn't just contain (e_1, r, e_2), but also (e_2, r_reverse, e_1).
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bkj
commented on August 27, 2024
Ah ok -- makes sense I think. Thanks!
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Related Issues (20)
- paper attribution HOT 2
- why do you have reverse triples in evaluation? HOT 3
- License HOT 1
- Why is self-adversarial negative sampling unfair? HOT 1
- Hyperparameters for Yago3-10 HOT 2
- Unable to reproduce results on WN18RR HOT 9
- Relation Prediction HOT 1
- Why set "padding_idx=0" in nn.Embedding HOT 2
- Do you only test tails in the evalution? HOT 1
- Could the one-way evaluation be a problem? HOT 2
- data progress, HOT 1
- Reverse flag implementation HOT 1
- Realistic Ranking Evaluation
- Unable to reproduce results on FB15k
- why not use 1-x score function.
- question about speed
- Wrong FB15k-237 folder name HOT 1
- Reopening evaluation issue HOT 8
- What different from torch==0.4.0 to torch==1.0.0? HOT 1
- arguments have invalid types: (numpy.ndarray) in "main.py", line 82 HOT 1
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