Castor

This is the common repo for PyTorch deep learning models by the Data Systems Group at the University of Waterloo.

Models

Predictions Over One Input Sequence

For sentiment analysis, topic classification, etc.

• Kim CNN: baseline convolutional neural network for sentence classification (Kim, EMNLP 2014)
• conv-RNN: convolutional RNN (Wang et al., KDD 2017)

Predictions Over Two Input Sequences

For paraphrase detection, question answering, etc.

• SM-CNN: Siamese CNN for ranking texts (Severyn and Moschitti, SIGIR 2015)
• MP-CNN: Multi-Perspective CNN (He et al., EMNLP 2015)
• NCE: Noise-Contrastive Estimation for answer selection applied on SM-CNN and MP-CNN
• IDF Baseline: IDF overlap between question and candidate answers

Setting up PyTorch

Copy and run the command at https://pytorch.org/ for your environment. PyTorch recommends the Anaconda environment, which we use in our lab.

The typical installation command is

conda install pytorch torchvision -c pytorch

Data and Pre-Trained Models

Data associated for use with this repository can be found at: https://git.uwaterloo.ca/jimmylin/Castor-data.git.

Pre-trained models can be found at: https://github.com/castorini/models.git.

Your directory structure should look like

.
├── Castor
├── Castor-data
└── models

For example (if you use HTTPS instead of SSH):

git clone https://github.com/castorini/Castor.git
git clone https://git.uwaterloo.ca/jimmylin/Castor-data.git
git clone https://github.com/castorini/models.git

Sourcing and pre-processing of input data for each model is described in the respective model/README.md's.