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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
41 lines
1.1 KiB
Markdown
41 lines
1.1 KiB
Markdown
# XML_CNN
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Implementation for XML Convolutional Neural Network for Document Classification of [XML-CNN (2014)](http://nyc.lti.cs.cmu.edu/yiming/Publications/jliu-sigir17.pdf) with PyTorch and Torchtext.
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## Model Type
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- rand: All words are randomly initialized and then modified during training.
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- static: A model with pre-trained vectors from [word2vec](https://code.google.com/archive/p/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.
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- non-static: Same as above but the pretrained vectors are fine-tuned for each task.
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## Quick Start
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To run the model on Reuters dataset on static just run the following from the Castor working directory.
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```
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python -m xml_cnn --dataset Reuters
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```
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The file will be saved in
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```
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xml_cnn/saves/best_model.pt
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```
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## Dataset
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We experiment the model on the following datasets.
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- Reuters: A multi-label document classification dataset.
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## Settings
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Adam is used for training.
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## TODO
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- Report hyperparameters and results after finetuning on other datasets like AAPD.
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