Sequence Generation Model for Multi-label Classification (COLING 2018)
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README.md Update README.md Sep 17, 2018
config.yaml
lr_scheduler.py Add files via upload Jun 7, 2018
optims.py
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README.md

Sequence Generation Model for Multi-label Classification

  • This is the code for our paper SGM: Sequence Generation Model for Multi-label Classification [pdf]
  • 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 = {Proceedings of the 27th International Conference on Computational
               Linguistics, {COLING} 2018, Santa Fe, New Mexico, USA, August 20-26,
               2018},
  pages     = {3915--3926},
  year      = {2018}
}