HILRecognizer

Get Started

HILRecognizer contains code and datasets for KDD 2019 submission:

• Zhang, S., He, L., Vucetic, S., Dragut, E., Human-in-the-loop ML Systems for Entity Extraction, KDD, 2019

To run the code, the following environment is required:

• python==2.7.6
• torch==0.3.1

Run 5 fold cross validation.

The 5-fold cross validation is used to select the best hyperparameters based on the weaklly labelled data, via random search technique. After the 5 fold cross validation, the best hyperparameter XX.pkl is output to the outfolder folder.

outfolder="experiments/position/kaggle_bound/pretrain" CUDA_VISIBLE_DEVICES="$dev" python s_train_bilstm_tagger.py --data data/position/testKaggle2.csv \ --save "$outfolder" --pooling max --partition loo --epochs 5 --cuda --batch-size 512 \ --tags o y --max_len 104 --label R.E.tag.gr6

Pretrain on weakly labelled data

CUDA_VISIBLE_DEVICES="$dev" python s_train_bilstm_tagger.py --data data/position/testKaggleAll.csv \ --save experiments/position/kaggle_bound/pretrain \ --params experiments/position/kaggle_bound/loo_R.E.tag_best_args.pkl \ --epochs 5 --cuda --batch-size 512 --tags o y --max_len 104 --label R.E.tag --run pretrain

Fine-tuninig pre-trained model with active learning

``aliter=50 # active learning iterations albs=20 # active learning batch size epoch=10 # active learning epochs best_args="loo_R.E.tag_best_args.pkl" # best args of pretrained model pretrain= "R.E.tag_testKaggleAll_pretrain_5.pt" # pretrained model outfolder="active_learning_cv_by_outlet_retag_pt5" # output folder

CUDA_VISIBLE_DEVICES="$dev" python s_train_bilstm_tagger.py --data data/position/testKaggle2.csv
--save experiments/position/kaggle_bound/"$outfolder"/
--params experiments/position/kaggle_bound/"$best_args"
--pretrain experiments/position/kaggle_bound/pretrain/"$pretrain"
--epochs 10 --cuda --partition outlet --batch-size 300 --tags o y --max_len 104 --label TagLabel
--fold "$fold" --run al --al_bs "$albs" --al_iter "$aliter" ``