QASystem

this is a QASystem implemented with BERT

install prerequisite packages

install with command

pip3 install -U tf-nightly-2.0-preview bert-for-tf2 flask flask-cors flask-socketio celery gevent
sudo apt install libboost-all-dev rabbitmq-server

celery and socketio need rabbit message queue, so launch it with command

sudo systemctl start rabbitmq-server

download with the following command.

bash downloads.sh

put the questions and answers in format as question_answer.txt's. and execute following command to convert the collected samples into dataset format.

make -C cc && make -C cc install
./cc/create_dataset -i question_answer.txt -o dataset

with directory dataset generated by the above command presented, finetune with the following to start finetune.

python3 Predictor.py

run the QA system server by

CUDA_VISIBLE_DEVICES='' python3 server.py

stop the server by Ctrl+C

firefox <ip>:5000

Name		Name	Last commit message	Last commit date
Latest commit History 146 Commits
cc		cc
pics		pics
static		static
templates		templates
BERT.py		BERT.py
Predictor.py		Predictor.py
QASystem.py		QASystem.py
README.md		README.md
config.py		config.py
cust_service_robot.sql		cust_service_robot.sql
downloads.sh		downloads.sh
query.py		query.py
question_answer.txt		question_answer.txt
server.py		server.py
start_workers.sh		start_workers.sh
stop_workers.sh		stop_workers.sh