This repo contains PyTorch deep learning models for document classification, implemented by the Data Systems Group at the University of Waterloo.

Models

• DocBERT : DocBERT: BERT for Document Classification (Adhikari et al., 2019)
• Reg-LSTM: Regularized LSTM for document classification (Adhikari et al., NAACL 2019)
• XML-CNN: CNNs for extreme multi-label text classification (Liu et al., SIGIR 2017)
• HAN: Hierarchical Attention Networks (Zichao et al., NAACL 2016)
• Char-CNN: Character-level Convolutional Network (Zhang et al., NIPS 2015)
• Kim CNN: CNNs for sentence classification (Kim, EMNLP 2014)

Each model directory has a README.md with further details.

Setting up PyTorch

Hedwig is designed for Python 3.6 and PyTorch 0.4. PyTorch recommends Anaconda for managing your environment. We'd recommend creating a custom environment as follows:

$ conda create --name castor python=3.6
$ source activate castor

And installing PyTorch as follows:

$ conda install pytorch=0.4.1 cuda92 -c pytorch

Other Python packages we use can be installed via pip:

$ pip install -r requirements.txt

Code depends on data from NLTK (e.g., stopwords) so you'll have to download them. Run the Python interpreter and type the commands:

>>> import nltk
>>> nltk.download()

Datasets

There are two ways to download the Reuters, AAPD, and IMDB datasets, along with word2vec embeddings:

Option 1. Our Wasabi-hosted mirror:

$ wget http://nlp.rocks/hedwig -O hedwig-data.zip
$ unzip hedwig-data.zip

Option 2. Our school-hosted repository, hedwig-data:

$ git clone https://github.com/castorini/hedwig.git
$ git clone https://git.uwaterloo.ca/jimmylin/hedwig-data.git

Next, organize your directory structure as follows:

.
├── hedwig
└── hedwig-data

After cloning the hedwig-data repo, you need to unzip the embeddings and run the preprocessing script:

cd hedwig-data/embeddings/word2vec 
tar -xvzf GoogleNews-vectors-negative300.tgz

If you are an internal Hedwig contributor using the machines in the lab, follow the instructions here.