Castor/xml_cnn

XML_CNN

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

Model Type

• 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.

Quick Start

To run the model on Reuters dataset on static just run the following from the Castor working directory.

python -m xml_cnn --dataset Reuters

The file will be saved in

xml_cnn/saves/best_model.pt

Dataset

We experiment the model on the following datasets.

• Reuters: A multi-label document classification dataset.

Settings

Adam is used for training.

TODO

• Report hyperparameters and results after finetuning on other datasets like AAPD.