Implementation of XML Convolutional Neural Network for Document Classification XML-CNN (2014) with PyTorch and Torchtext.
To run the model on the Reuters dataset, just run the following from the working directory:
python -m models.xml_cnn --mode static --dataset Reuters --batch-size 32 --lr 0.01 --epochs 30 --dropout 0.5 --dynamic-pool-length 8 --seed 3435
The best model weight will be saved in
To test the model, you can use the following command.
python -m models.xml_cnn --dataset Reuters --mode static --batch-size 32 --dynamic-pool-length 8 --trained-model models/xml_cnn/saves/Reuters/best_model.pt --seed 3435
- rand: All words are randomly initialized and then modified during training.
- static: A model with pre-trained vectors from word2vec. All words -- including the unknown ones that are initialized with zero -- are kept static and only the other parameters of the model are learned.
- non-static: Same as above but the pretrained vectors are fine-tuned for each task.
We experiment the model on the following datasets.
- Reuters (ModApte)
Adam is used for training.