Comments (6)
Hi,
You can follow this nice script to split test data into 1-to-1, 1-to-N, N-to-1, and N-to-N: https://github.com/thunlp/OpenKE/blob/OpenKE-PyTorch/benchmarks/FB15K/n-n.py
And set them as your test data.
For prediction head and prediction tail, you can set use test_dataset_list to [test_dataloader_head] or [test_dataloader_tail].
from knowledgegraphembedding.
Hello. I used the script and create the text files of 1-to-1, 1-to-N, N-to-1, and N-to-N. Where I apply this, will you please elaborate it:
prediction head and prediction tail, you can set use test_dataset_list to [test_dataloader_head] or [test_dataloader_tail]?
Head and tail as (test_dataloader_head and test_dataloader_tail) are not in a separate files, so where I can find it?
from knowledgegraphembedding.
Hi,
You can
use test_dataloader_head + 1-to-1 and test_dataloader_tail+ 1-to-1 to evaluate 1-to-1 in prediction head and prediction tail,
use test_dataloader_head + 1-to-N and test_dataloader_tail+ N-to-1 to evaluate 1-to-N in prediction head and prediction tail,
use test_dataloader_head + N-to-1 and test_dataloader_tail+ 1-to-N to evaluate N-to-1 in prediction head and prediction tail,
and use use test_dataloader_head + N-to-N and test_dataloader_tail+ N-to-N to evaluate N-to-1 in prediction head and prediction tail.
Therefore, to get all 8 evaluation scores in this table, you will need to run the model for 8 times.
from knowledgegraphembedding.
I understand a bit but having one confusion. what is test_dataloader_head and test_dataloader_tail?
Where I can find it? or I have to create it from train2id by separating all heads and tails?
from knowledgegraphembedding.
Hi, it's in this line:
KnowledgeGraphEmbedding/codes/model.py
Line 374 in 2442e8b
from knowledgegraphembedding.
@Edward-Sun Thanks Sir.
from knowledgegraphembedding.
Related Issues (20)
- GPU Out Of Memory HOT 2
- Pretrained models
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- When I train a more huge dataset by model RotatE, always show the question "CUDA out of memory" ? HOT 1
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- regarding RELATION CATEGORY in FB15k HOT 1
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- Init embedding random OR by bert encode from entity description text HOT 1
- CUDA out of memory (resolved) and method to make RotatE run faster HOT 2
- L3 regularization
- Memory consumption issue HOT 1
- How to designate dimension of embeddings
- Three questions HOT 1
- Why start = 4
- "IndexError: index out of range in self" when I train on my own knowledge graph. Can anyone help me with this? Thanks a lot!
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