Certainly! Below is a consolidated readme.md
file that includes information from various sections:
# Chatbot using Seq2Seq LSTM Models
This repository contains code for building a Chatbot using Seq2Seq LSTM models in Keras.
## Introduction
In this project, we leverage the power of Seq2Seq LSTM models to create a functional Chatbot capable of answering user questions. This approach can be applied to various domains, such as e-commerce or educational websites.
## Getting Started
### Prerequisites
Make sure you have the following dependencies installed:
- TensorFlow
- Keras
- bnlp_toolkit
- stopwordsiso
- wikipedia
Install them using the following command:
```bash
pip install -r requirements.txt
Download the chatterbot/english dataset and extract it into the data
directory.
# Example download command
wget https://github.com/shubham0204/Dataset_Archives/blob/master/chatbot_nlp.zip?raw=true -O chatbot_nlp.zip
unzip chatbot_nlp.zip
Run the Jupyter notebook ChatBot_With_Seq2Seq.ipynb
to train the Seq2Seq LSTM model. The model will be saved as model.h5
.
jupyter notebook ChatBot_With_Seq2Seq.ipynb
After training, you can interact with the Chatbot by running the provided inference code. Enter a question, and the Chatbot will generate an appropriate response.
python chatbot_inference.py
- The
chatbot_inference.py
script allows interactive chatting with the trained Chatbot. - The
bengalianalyzer
library is used for Bangla text analysis in certain parts of the code.
This project is licensed under the MIT License - see the LICENSE file for details.
Feel free to adjust and modify the content based on your specific project details and preferences.