This repository implements SIHTC, an optimized model via structural entropy and singular smoothing for hierarchical text classification.

For details about data acquisition, processing, and baseline parameter settings, please refer to HPT.

Checkpoints are in ./checkpoints/DATA-NAME. Two checkpoints are kept based on macro-F1 and micro-F1 respectively (checkpoint_best_macro.pt, checkpoint_best_micro.pt). The training requires the modification of parameters based on the dataset. --seloss-wight is for the wight of structural entropy loss, and --label-loss-wight, --hie-label-loss-wight are for the wight of singular value smoothing regularization loss. We take the main results as the average of six random experiments.

python train.py --name test --batch 30 --data WebOfScience --seloss-wight 0.05 --label-loss-wight 0.05 --hie-label-loss-wight 0.05
python train.py --name test --batch 30 --data rcv1 --seloss-wight 0.1 --label-loss-wight 0.005 --hie-label-loss-wight 0.005
python train.py --name test --batch 30 --data NYT --seloss-wight 0.01 --label-loss-wight 0.005 --hie-label-loss-wight 0.005

Use --extra _macro or --extra _micro to choose from using checkpoint_best_macro.pt or checkpoint_best_micro.pt respectively.

python test.py --name WebOfScience-test