Sequence Generation Model for Multi-label Classification

This is the code for our paper SGM: Sequence Generation Model for Multi-label Classification
Be careful: the provided code is based on the RCV1-V2 dataset. If you need to run the code on other datasets, please correspondly modify all program statements that relate to the specific name of the dataset.

Datasets

RCV1-V2
AAPD

Two datasets are available at https://drive.google.com/file/d/18-JOCIj9v5bZCrn9CIsk23W4wyhroCp_/view?usp=sharing

Requirements

Ubuntu 16.0.4
Python 3.5
Pytorch 0.3.1

Reproducibility

We provide the pretrained checkpoints of the SGM model and the SGM+GE model on the RCV1-V2 dataset to help you to reproduce our reported experimental results. The detailed reproduction steps are as follows:

Please download the RCV1-V2 dataset and checkpoints first by clicking on the link provided above, then put them in the folder ./data/data/
Preprocessing: python3 preprocess.py
Predict: python3 predict.py -gpus id -log log_name

Preprocessing

python3 preprocess.py

Remember to download the dataset and put them in the folder ./data/data/

Training

python3 train.py -gpus id -log log_name

Evaluation

python3 predict.py -gpus id -restore checkpoint -log log_name

Citation

If you use the above code or the AAPD dataset for your research, please cite the paper:

@inproceedings{YangCOLING2018,
   author = {Pengcheng Yang and Xu Sun and Wei Li and Shuming Ma and Wei Wu and Houfeng Wang},
   title = {SGM: Sequence Generation Model for Multi-label Classification},
   booktitle = {{COLING} 2018},
   year = {2018}
}

LloydZhang / SGM