- Download PubMed 20k/200k RCT numbers replaced with at sign from https://github.com/Franck-Dernoncourt/pubmed-rct .
- Put glove.6B.300d.txt, train.txt, dev.txt and test.txt into the data/ .
- cd src/ and run the following command to preprocess and generate training data.
python make_dataset.py ../data/ - Run the following command to prepapre model folder. Feel free to open the config.json to tune some hyperparameters.
mkdir ../models/your_model_folder cp ../models/hnn/config.json ../models/your_model_folder - Run the following command to start training.
python train.py ../models/your_model_folder/ - Trained model will saved in the folder 'your_model_folder/'
- Run the following shell
./download.sh - cd src/
- Run the following command to predict test.txt. You are able to change x in '--epoch x' to the best epochs you ran.
python predict.py ../models/hnn/ --epoch 7 --input_mode 1 --input_dir ../data/test.pkl --output_dir result.txt or python predict.py ../models/your_model_folder/ --epoch 7 --input_mode 1 --input_dir ../data/test.pkl --output_dir result.txt - Run the following command to predict input.
python predict.py ../models/hnn/ --epoch 7 or python predict.py ../models/your_model_folder/ --epoch 7 - Type in the abstract and it will output the result. Try input the following abstract.
To evaluate the performance ( efficacy , safety and acceptability ) of a new micro-adherent absorbent dressing ( UrgoClean ) compared with a hydrofiber dressing ( Aquacel ) in the local management of venous leg ulcers , in the debridement stage .$$$A non-inferiority European randomised controlled clinical trial ( RCT ) was conducted in @ centres , on patients presenting with venous or predominantly venous , mixed aetiology leg ulcers at their sloughy stage ( with more than @ % of the wound bed covered with slough at baseline ) .$$$Patients were followed over a @-week period and assessed weekly .$$$The primary judgement criteria was the relative regression of the wound surface area after the @-week treatment period .