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TransE-PyTorch

Implementation of TransE [1] model in PyTorch.

Table of Contents

  1. Results
    1. Datasets
      1. FB15k
  2. Usage
    1. Training
      1. Options
    2. Unit tests
  3. References

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=128
Negative sampling impact over time

Negative 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_gpu

Training

python3 main.py --nouse_gpu --dataset_path=<path_to_your_dataset>

Options

To see possible configuration options run help

python3 main.py --help

Unit tests

python3 -m unittest discover -p "*_test.py"

References

[1] Bordes et al., "Translating embeddings for modeling multi- relational data," in Adv. Neural Inf. Process. Syst., 2013