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https://github.com/microsoft/HittER
Hierarchical Transformers for Knowledge Graph Embeddings (EMNLP 2021)
https://github.com/microsoft/HittER
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Hierarchical Transformers for Knowledge Graph Embeddings (EMNLP 2021)
- Host: GitHub
- URL: https://github.com/microsoft/HittER
- Owner: microsoft
- License: mit
- Created: 2021-10-07T20:57:51.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2024-07-25T11:00:21.000Z (4 months ago)
- Last Synced: 2024-08-02T13:19:05.364Z (3 months ago)
- Language: Python
- Homepage:
- Size: 122 KB
- Stars: 71
- Watchers: 7
- Forks: 15
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Security: SECURITY.md
Awesome Lists containing this project
README
HittER
Hierarchical Transformers for Knowledge Graph Embeddings
HittER generates embeddings for knowledge graphs and performs link prediction using a hierarchical Transformer model.
It will appear in EMNLP 2021 ([arXiv version](https://arxiv.org/abs/2008.12813)).
## Installation
The repo requires python>=3.7, anaconda and a new env is recommended.
``` sh
conda create -n hitter python=3.7 -y # optional
conda activate hitter # optional
git clone [email protected]:microsoft/HittER.git
cd HittER
pip install -e .
```
### Data
First download the standard benchmark datasets using the commands below. Thanks [LibKGE](https://github.com/uma-pi1/kge) for providing the preprocessing scripts and hosting the data.
``` sh
cd data
sh download_standard.sh
```
### Training
Configurations for the experiments are in the `/config` folder.
``` sh
python -m kge start config/trmeh-fb15k237-best.yaml
```
The training process uses DataParallel in all visible GPUs by default, which can be overrode by appending `--job.device cpu` to the command above.
### Evaluation
You can evaluate the trained models on dev/test set using the following commands.
``` sh
python -m kge eval
python -m kge test
```
Pretrained [models](https://github.com/microsoft/HittER/releases/tag/v1.0.0) are also released for reproducibility.
### HittER-BERT QA experiments
QA experiment-related data can be downloaded from the release.
``` sh
git submodule update --init
cd transformers
pip install -e .
```
Run experiments
``` sh
> python hitter-bert.py --help
usage: hitter-bert.py [-h] [--dataset {fbqa,webqsp}] [--filtered] [--hitter]
[--seed SEED]
[exp_name]
positional arguments:
exp_name Name of the experiment
optional arguments:
-h, --help show this help message and exit
--dataset {fbqa,webqsp}
fbqa or webqsp
--filtered Filtered or not
--hitter Use pretrained HittER or not
--seed SEED Seed number
```
## Contributing
This project welcomes contributions and suggestions. Most contributions require you to agree to a
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
When you submit a pull request, a CLA bot will automatically determine whether you need to provide
a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
contact [[email protected]](mailto:[email protected]) with any additional questions or comments.
## Trademarks
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft
trademarks or logos is subject to and must follow
[Microsoft's Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general).
Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.
Any use of third-party trademarks or logos are subject to those third-party's policies.
## Citation
```
@inproceedings{chen-etal-2021-hitter,
title = "HittER: Hierarchical Transformers for Knowledge Graph Embeddings",
author = "Chen, Sanxing and Liu, Xiaodong and Gao, Jianfeng and Jiao, Jian and Zhang, Ruofei and Ji, Yangfeng",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2021",
publisher = "Association for Computational Linguistics"
}
```