https://github.com/ruvenguna94/dialogue-summary-use-case
GEN AI use case: dialogue summary. This notebook is extracted from the course Generative AI with Large Language Models. It is used to understand how input text can affect model performance.
https://github.com/ruvenguna94/dialogue-summary-use-case
dialogue-summarization flan-t5 generative-ai huggingface one-shot-learning transformers zero-shot-learning
Last synced: 16 days ago
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GEN AI use case: dialogue summary. This notebook is extracted from the course Generative AI with Large Language Models. It is used to understand how input text can affect model performance.
- Host: GitHub
- URL: https://github.com/ruvenguna94/dialogue-summary-use-case
- Owner: RuvenGuna94
- Created: 2024-12-12T07:28:16.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-01-16T10:47:31.000Z (3 months ago)
- Last Synced: 2025-02-13T03:54:44.920Z (2 months ago)
- Topics: dialogue-summarization, flan-t5, generative-ai, huggingface, one-shot-learning, transformers, zero-shot-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 84 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
## Project Overview
This repository contains the `dialogue_summary.ipynb` notebook, which demonstrates techniques for summarizing dialogues using the FLAN-T5 models. The project leverages libraries, such as `transformers` and `datasets`, for working with pre-trained models and datasets.
## Features
- Summarization of dialogues using pre-trained language models.
- Flexible customization of model parameters and datasets.
- Visualization and analysis of text results.
## Prerequisites
Ensure you have the following installed on your system:
- Python 3.8 or later
- Jupyter Notebook or JupyterLab
## Installation
1. Clone the repository:
```bash
git clone [https://github.com/your-username/dialogue_summary.git](https://github.com/RuvenGuna94/Dialogue-Summary-Use-Case)
cd dialogue_summary
```
2. Set up a virtual environment (optional but recommended):
```bash
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
3. Install the required dependencies:
```bash
pip install -r requirements.txt
```
## Usage
1. Launch the Jupyter Notebook:
```bash
jupyter notebook
```
2. Open `dialogue_summary.ipynb` in your browser and follow the instructions in the notebook.
## Contributing
Feel free to fork the repository and submit pull requests. Suggestions and improvements are welcome!
## License
This project is licensed under the MIT License. See the LICENSE file for details.
## Developer Log
### 12 Dec 2024
- Created new repo
- Added in Jupyter Notebook
### 13 Dec 2024
- Updated repo description
- Completed section 1 of the use case
### 14 Dec 2024
- Configured local env to run code
- Installed required conda env and kernel
- Completed the following
- Package install
- Load libraries
- load dataset & exploratory analysis
- load model & associated tokenizer
- Performed sample text encoding and decoding
### 15 Dec 2024
- Completed notebook with zero shot, one shot and few shot inference code