--author: zhengzhe
--date: 2022.10.26

• Description: Code and dataset for the paper named "Text Classification-Based Approach for Evaluating and Enhancing Machine Interpretability of Building Codes".
• This is a Pytorch-based Bert Chinese text classification approach.
• Bert models thanks to Bert-Chinese-Text-Classification-Pytorch
• Other models thanks to Chinese-Text-Classification-Pytorch

python 3.7
torch 1.12.1+cu116 boto3 1.24.28
matplotlib 3.5.3
tqdm
sklearn
tensorboardX

• Description: Chinese rule dataset including seven categories are established to classify the interpretability level of each rule in a building code
• The original labeled dataset can be found in CivilRules/dataset
• The training, validation, and test dataset can be found in CivilRules/data

• The original Bert model can be found in google drive
  • Please put the original Bert model in ./bert_pretrain
• The further pretrained domain-specific Bert model (RuleBERT) can be found in google drive
  • Please put the RuleBERT model in ./bert_pretraindc

• The well trained BERT models (.ckpt files) can be found in google drive
• Please put these models in ./CivilRules/save_dict

• The well-trained models (TextCNN, TextRNN, TextRNN-Att, Transformers) can be found in google drive
• Reproduce the result can use the code from Chinese-Text-Classification-Pytorch

• assert the bert models and the finetune models have been put into the right place
• put test dataset (test.txt) in to ./CivilRules/data

# validate the bert model weighted F1 score
python test.py --model bert
# validate the RuleBERT model weighted F1 score
python test.py --model bertDC

• prepare your own test dataset in to ./CivilRules/data
• modify the dataset, learning_rates, batch_sizes in grid_search.py

# to finetune bert model
python grid_search.py --model bert
# to finetune RuleBERT model
python grid_search.py --model bertDC

• prepare your own prediction dataset (predict.txt) and named it to dev.txt, and then put it in to ./CivilRules/data
• modify the dataset in application.py
• prepare well-trained bert model in to ./CivilRules/save_dict

python application.py --model bert
python application.py --model bertDC

• the result will be saved in ./CivilRules/predict