Features

• generate summary on given text

Train and Use the model generate by BertSum

please follow this documents about how to run the application on Kubernetes

Installation In A Nutshell (on localhost)

1. clone this project to your local disk

 git clone https://github.com/herre0/EssayScoringSystem.git

2. Install Flask
3. Install ROUGE-1.5.5

• Install libxml-parser-perl, it is essential for installing ROUGE-1.5.5

  sudo apt-get install libxml-parser-perl

• and make sure you can run this, which means the ROUGE is successfully installed

  ./runROUGE-test.pl

4. Install pyrouge

 git clone https://github.com/bheinzerling/pyrouge.git
 cd pyrouge
 pip install -e .

• Additional information can be found here about how to install pyrouge and running the test

5. Please install pytorch 1.1.0 with this comand

• GPU

  conda install pytorch==1.1.0 torchvision==0.3.0 cudatoolkit=10.0 -c pytorch

• CPU Only

  conda install pytorch-cpu==1.1.0 torchvision-cpu==0.3.0 cpuonly -c pytorch

6. Download pretrained-bert-model, and unzip it to a location you like
7. Change the path in BertParent.py in summarizer folder

 self.model = BertModel.from_pretrained('/path/to/bert-large-uncased')

8. Run python app.py in the flask-summary directory.
9. Start web server by running python app.py while in the server_example directory.
10. Browse the examples at 0.0.0.0:5000 using a browser. (defaults to port 5000)

Screen Demo