apucs/chatbot

Chatbot Project

Introduction:

In the Project, we are about to use efficient Transformer(Reformer) to generate a dialogue between two bots using Trax.

The dataset that we have used is called MultiWoz dataset. The dataset that we have used has more than 10,000 human annotated dialogues and spans multiple domains and topics. Some dialogues include multiple domains and others include single domains.

Project Description

Root directory of this project contains:

• 2 sub-folders

• several [.py] files

• 1 text files containing all the requirements of this project

• 1 readme.md file containing all the necessary instructions.

• Some test image examples

Details about the folders and files:

• data(folder): Contains all the vocabulary and dataset files that will be used in this project.

• model(folder): Contains out train, and validation checkpoints, model configuration file and our trained model will be saved here.

• config.py: Configuration file for our project data paths and variables.

• utils.py : Contains different helper functions.

• process.py : python script to process and generate our train and validation data streams.

• model.py : Contains python scripts to define our model and model helper functions.

• train.py: Training script to train the model.

• inference.py: Here we can test our model by giving a starting sentence and maximum len of our conversation which will generate the conversation by applying the model.

Instructions:

Before starting we need to satisfy all the dependencies. For that reason need to execute the following command. (All the commands need to be executed from the root folder)

• Install the dependencies:
  pip install -r requirements.txt

Before start the training we need to process our dataset into the correct format. and then we will start our training.

• To process and start training the dataset:
  python train.py

• To evaluate the trained model:

• Our inference script takes two argument parser parameters to infer from the trained model.

• parameter 1(--s_sen): Our staring sentence starts with a space followed by (person 1:) delimeter and end with (person 2:) delimeter.

• parameter 2(--max_len): Maximum length of our generated conversation.

• Running inference script:
  !python inference.py --s_sen ' Person 1: starting/line/of/the/convo Person 2: ' --max_len 200

Inference

Some generated conversation with the trained model: