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https://github.com/islam-hady9/smartai_customersupport
Smart Customer Support Assistant
https://github.com/islam-hady9/smartai_customersupport
customer-support gpt-2 natural-language-processing python pytorch scikit-learn transformers
Last synced: about 3 hours ago
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Smart Customer Support Assistant
- Host: GitHub
- URL: https://github.com/islam-hady9/smartai_customersupport
- Owner: Islam-hady9
- Created: 2024-08-09T00:47:08.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2024-08-09T08:57:20.000Z (about 2 months ago)
- Last Synced: 2024-09-23T06:03:14.712Z (4 days ago)
- Topics: customer-support, gpt-2, natural-language-processing, python, pytorch, scikit-learn, transformers
- Language: Jupyter Notebook
- Homepage:
- Size: 4.88 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# SmartAI_CustomerSupport
SmartAI_CustomerSupport is an AI-driven customer support assistant built using GPT-2. The project demonstrates how to fine-tune a pre-trained language model on custom customer support data and integrate it into a simple interactive environment.
## Features
- Fine-tunes GPT-2 on customer support data.
- Generates contextually relevant responses to customer queries.
- Interactive Jupyter Notebook for experimentation and testing.
## Installation
1. Clone the repository:
```bash
git clone https://github.com/Islam-hady9/SmartAI_CustomerSupport.git
cd SmartAI_CustomerSupport
```
2. Create and activate a conda environment:
```bash
conda create --name smartai_customersupport python=3.8
conda activate smartai_customersupport
```
3. Install the required dependencies:
```bash
pip install -r requirements.txt
```
## Usage
1. **Open the Project:**
- Start by opening the `SmartAI_CustomerSupport.ipynb` Jupyter Notebook in your preferred environment (e.g., Jupyter Lab or Jupyter Notebook).
2. **Run the Notebook Cells:**
- Follow the sequential execution of the cells in the notebook. This will:
- Load the pre-trained GPT-2 model and tokenizer.
- Tokenize sample queries.
- Fine-tune the model if necessary.
- Generate and display responses to the input queries.
3. **Interact with the Model:**
- Modify the input queries within the notebook to test the model with different customer support questions.
- Observe and analyze the generated responses for evaluation.
4. **Experiment and Modify:**
- Use the notebook to experiment with different hyperparameters, input data, or model configurations to better suit your needs.
## Contributing
Contributions are welcome! Please fork the repository and submit a pull request.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.