Comments (2)
@AndRossi All the technical reasons have been pointed out in the author's comment above.
GraphVite is targeted at speed and scalability so there is slight difference in performance. Actually such gap could be easily covered by some hyperparameter hacking (e.g. double the dimension of embedding or training length), but we keep the original setting to make it a clean system work.
If you want to use KGE as an off-the-shelf tool, it is recommended to use GraphVite. If you want to develop new SOTA methods for KGE, it is recommended to use this repo.
from knowledgegraphembedding.
Hi,
Thank you for your interest in both projects. The essential differences between this implementation and GraphVite implementation are listed as following:
- GraphVite uses ASGD rather than mini-batch SGD,the gradient information in GraphVite is relatively noisier.
- Considering the numeric stability in ASGD, GraphVite reduces the number of negative samples from 256 to 64.
- For a fair comparison with other large-scale KGE systems, the reported benchmark performance of GraphVite is undertrained. GraphVite can achieve better performance when using a single GPU and train for a longer time.
from knowledgegraphembedding.
Related Issues (20)
- GPU Out Of Memory HOT 2
- Pretrained models
- RuntimeError: The size of tensor a (2000) must match the size of tensor b (1000) at non-singleton dimension 2 HOT 3
- confused about implementation of DistMult HOT 1
- how do you generate the embedding? Is it a random walk or something? I mean what is the main algorithm for embedding. HOT 1
- How to split the train/valid/test HOT 1
- When I train a more huge dataset by model RotatE, always show the question "CUDA out of memory" ? HOT 1
- N-1 and 1-N relations HOT 3
- hi,a priblem about multi GPU training HOT 1
- confused about implementation of 1-1, 1-n, n-1, n-n HOT 1
- regarding RELATION CATEGORY in FB15k HOT 1
- Regarding the direction of the rotation HOT 1
- 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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from knowledgegraphembedding.