TransE-PyTorch
Implementation of TransE [1] model in PyTorch.
Table of Contents
Results
Datasets
FB15k
| Source/Metric | Hits@1 (raw) | Hits@3 (raw) | Hits@10 (raw) | MRR (raw) |
|---|---|---|---|---|
| Paper [1] | X | X | 34.9 | X |
| TransE-PyTorch | 11.1 | 25.33 | 46.53 | 22.29 |
python3 main.py --dataset_path=<path_to_fb15k_dataset> --epochs=50000 --batch_size=128Negative sampling impact over time
X axis - epoch id
Y axis - % of samples with nonzero loss
Usage
Synthetic data
For fast debugging/feedback loop use synthetic data from synth_data directory.
python3 main.py --nouse_gpuTraining
python3 main.py --nouse_gpu --dataset_path=<path_to_your_dataset>Options
To see possible configuration options run help
python3 main.py --helpUnit tests
python3 -m unittest discover -p "*_test.py"