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Ashutosh-Adhikari
650882fb6e
* Add Reuters option in common.dataset * Add Reuters option in common.dataset * Add HAN model * Add XML-CNN * Add HAN * Add Hierarchical tokenization for Reuters * Add README for HAN * Add XML Readme * Update HAN Readme
1.1 KiB
1.1 KiB
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.