Master Thesis Project
07/09/2020 - Question answering agent version 2.0.
The project (API) is developed to be run using the flask and waitress library. The application can be ran with the following command 'python3 waitress_server.py' The file 'json_post_example.py' illustrates how the agent can be called through the api. There is also an interactive version of the agent in file 'run_agent.py'.
The latest trained GPT-2 generative model can be downloaded from: https://drive.google.com/open?id=1bamDAUSHM8ye6xzs51-hi9un03DwjOy5
Trained classifiers can be downloaded here: https://drive.google.com/file/d/1coJjE3UL7feooR2x-g6zwedkUPGcKJQA/view?usp=sharing
The project initially used the gpt_2_simple library, but due to concurrent generation problems and due to the library not being optimized for fast response time (for chatbots), the project now uses the script "gpt2_specific_gen.py" to generate. The script is an adapted variation of "interactive_conditional_samples.py" from OpenAI and the "gpt_2.py" script from the gpt_2_simple library.
git clone https://github.com/lcebear/memoryDialogueBot.gitCreate a virtual python environment
python3 venv path/to/envInstall requirements
pip install -r requirements.txtDownload additional packages
python -m spacy download en_core_web_lgdownload nltk packages 'stopwords', 'punkt', 'vader_lexicon', 'averaged_perceptron_tagger' by using nltk.download()
import nltk
nltk.download('punkt')
nltk.download('stopwords')
nltk.download('vader_lexicon')
nltk.download(averaged_perceptron_tagger')
...Download latest GPT-2 generative model to project folder.
Download trained classifiers to project folder.
python3 -m run_agent.py python3 waitress_server.py-> Example API usage in json_post_example.py
Remember to change the secret key (app.secret_key) in the 'agent_api' file to some random bytes.
Conversational Chatbots with Memory-based Question and Answer Generation: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-171927
@mastersthesis{lundellvinkler2020conversational, title={Conversational Chatbots with Memory-based Question and Answer Generation}, author={Lundell Vinkler, Mikael and Yu, Peilin}, year={2020}, school={Link\={o}ping University } }