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https://github.com/nafisrayan/lstm-chatbot-from-scratch
LSTM Chatbot 🤖from scratch
https://github.com/nafisrayan/lstm-chatbot-from-scratch
ai artificial-intelligence chatbot datasets lstm machine-learning ml nlp python pytorch
Last synced: about 5 hours ago
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LSTM Chatbot 🤖from scratch
- Host: GitHub
- URL: https://github.com/nafisrayan/lstm-chatbot-from-scratch
- Owner: NafisRayan
- Created: 2024-07-16T12:53:32.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2024-07-16T14:08:16.000Z (4 months ago)
- Last Synced: 2024-07-17T15:48:43.307Z (4 months ago)
- Topics: ai, artificial-intelligence, chatbot, datasets, lstm, machine-learning, ml, nlp, python, pytorch
- Language: Jupyter Notebook
- Homepage:
- Size: 1.9 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# LSTM-Chatbot-from-scratch
LSTM Chatbot 🤖from scratch
This repository contains a comprehensive guide and implementation for building a chatbot from scratch using Long Short-Term Memory (LSTM) networks. The project is designed to help users understand the intricacies of developing intelligent conversational agents through practical application.
## Table of Contents
- [Introduction](#introduction)
- [Getting Started](#getting-started)
- [Prerequisites](#prerequisites)
- [Usage](#usage)
- [Dependencies](#dependencies)
- [Contributing](#contributing)
- [License](#license)
## Introduction
The goal of this project is to provide a hands-on approach to understanding LSTM networks, a type of recurrent neural network (RNN) capable of learning long-term dependencies. By implementing a chatbot, we aim to demonstrate how LSTMs can be applied to natural language processing tasks, enabling machines to engage in conversations similar to human interactions.
## Getting Started
To get started with this project, clone the repository and navigate to the project directory. Ensure you have Python 3.x and Jupyter Notebook installed on your system. If you don't have Jupyter Notebook, you can install it via pip:
```bash
pip install jupyter
```
Then, launch Jupyter Notebook and open the `LSTM_Chatbot_from_scratch.ipynb` file to start exploring the project.
## Prerequisites
- Python 3.x
- Jupyter Notebook
- Basic knowledge of Python programming
- Familiarity with machine learning concepts
## Usage
Follow the instructions in the `LSTM_Chatbot_from_scratch.ipynb` notebook. The notebook is structured into several sections:
1. **Data Preparation**: Downloading and preprocessing the dataset.
2. **Model Building**: Constructing the LSTM model architecture.
3. **Training**: Training the model on the prepared dataset.
4. **Evaluation**: Assessing the model's performance and making predictions.
Execute each cell in the notebook sequentially to replicate the project.
## Dependencies
This project relies on several Python packages, including:
- os
- sys
- tempfile
- urllib.request
- urllib.parse
- urllib.error
- zipfile
- tarfile
- shutil
Additionally, you'll need machine learning libraries like TensorFlow or PyTorch for building and training the LSTM model.
## Contributing
We welcome contributions from everyone. Whether it's a bug fix, new features, or improvements to documentation, we appreciate your efforts. Here's how you can contribute:
### Reporting Issues
If you encounter a bug or think something is missing, please create an issue describing the problem. Make sure to provide as much detail as possible, including steps to reproduce the issue and any error messages.
### Submitting Pull Requests
1. Fork the Repository
2. Clone your forked repository to your local machine.
3. Create a branch for your feature or fix.
bash git checkout -b my-feature
4. Make your changes and commit them.
bash git commit -am 'Implemented my awesome feature'
5. Push your branch to your forked repository.
bash git push origin my-feature
6. Go to your forked repository on GitHub and select your branch.
7. Click on "New pull request".
8. Fill out the form and click on "Create pull request".
### Code Style
- We use PEP 8 style guide for Python code.
- Use meaningful names for variables, functions, and classes.
- Add comments where necessary to explain why certain decisions were made.
### Testing
Before submitting a pull request, please test your changes thoroughly. Consider adding tests to cover your changes, especially if you're modifying existing functionality.
### Documentation
If your changes affect the documentation, please update the README and any other relevant documents accordingly.
Thank you for contributing!