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Michael Tu
f7a0167b81
* Refactor main README * Update Anserini Dependency docs * Update idf baseline and Kim CNN docs to use Castor-data * Update remaining READMEs to reference Castor-data * Change default path from data to Castor-data * Fix wrong order of embeddings path
Castor
Deep learning for information retrieval with PyTorch.
Models
Baselines
- IDF Baseline: IDF overlap between question and candidate answers
Deep Learning Models
- SM-CNN: Ranking short text pairs with Convolutional Neural Networks
- Kim CNN: Sentence classification using Convolutional Neural Networks
- MP-CNN: Sentence pair modelling with Multi-Perspective Convolutional Neural Networks
- NCE: Noise-Contrastive Estimation for answer selection applied on SM-CNN and MP-CNN
- conv-RNN: Convolutional RNN for text modelling
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.