Awesome
order-embeddings-wordnet
Code for the hypernym completion experiment from the paper "Order-Embeddings of Images and Language". See the other repo for the caption-image ranking and textual entailment experiments.
Dependencies
- Python 2 with a recent version of Numpy and nltk 3.0 for easy access to WordNet.
- Torch7 with the argparse package.
Create Datasets
Run
python preprocessWordnet.py
th createDatasets.lua
Training the Model
To train with default hyperparameters (the order-embedding model from the paper), run
th main.lua --epochs 20 --name "myfirstmodel"
or train your own version by setting any of the the flags in main.lua.
The resulting weights are stored in weights.t7. You can view traces of training
and validation error by navigating to the vis_training directory, running
python -m SimpleHTTPServer
and pointing your browser to the server (usually localhost:8000).
Reference
If you found this code useful, please cite the following paper:
Ivan Vendrov, Ryan Kiros, Sanja Fidler, Raquel Urtasun. "Order-Embeddings of Images and Language." arXiv preprint arXiv:1511.06361 (2015).
@article{vendrov2015order,
title={Order-embeddings of images and language},
author={Vendrov, Ivan and Kiros, Ryan and Fidler, Sanja and Urtasun, Raquel},
journal={arXiv preprint arXiv:1511.06361},
year={2015}
}