schangpi / cove

Context Vectors (CoVe)

This repo provides the best MT-LSTM from the paper Learned in Translation: Contextualized Word Vectors (McCann et. al. 2017). For a high-level overview of why CoVe are great, check out the post.

example.py uses torchtext to load the Stanford Natural Language Inference Corpus and GloVe.

It uses a PyTorch implementation of the MTLSTM class in mtlstm.py to load a pretrained encoder, which takes in sequences of vectors pretrained with GloVe and outputs CoVe.

Running with Docker

Install Docker. Install nvidia-docker if you would like to use with with a GPU.

docker pull bmccann/cove   # pull the docker image
docker run -it cove        # start a docker container
python /cove/test/example.py

Running without Docker

Install PyTorch.

git clone https://github.com/salesforce/cove.git # use ssh: [e-mail]:salesforce/cove.git
cd cove
pip install -r requirements.txt
python setup.py develop
python test/example.py

References

If using this code, please cite:

B. McCann, J. Bradbury, C. Xiong, R. Socher, Learned in Translation: Contextualized Word Vectors

@article{McCann2017LearnedIT,
  title={Learned in Translation: Contextualized Word Vectors},
  author={Bryan McCann and James Bradbury and Caiming Xiong and Richard Socher},
  journal={arXiv preprint arXiv:1708.00107},
  year={2017}
}

Contact: [e-mail]