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Implementation of XML Convolutional Neural Network for Document Classification XML-CNN (2014) with PyTorch and Torchtext.

Quick Start

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/ --seed 3435

Model Types

  • 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)
  • AAPD


Adam is used for training.

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