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https://github.com/himalayaashish/text_summarizer
This project demonstrates how to leverage transformer-based models to perform text summarization on conversation data using the Hugging Face transformers library. A pre-trained BART-large-XSum model is utilized to summarize long conversations. The summarizer is optimized to run on a GPU, ensuring fast inference and efficient tokenization handling.
https://github.com/himalayaashish/text_summarizer
json-data nlu tensorflow2 transformer
Last synced: 28 days ago
JSON representation
This project demonstrates how to leverage transformer-based models to perform text summarization on conversation data using the Hugging Face transformers library. A pre-trained BART-large-XSum model is utilized to summarize long conversations. The summarizer is optimized to run on a GPU, ensuring fast inference and efficient tokenization handling.
- Host: GitHub
- URL: https://github.com/himalayaashish/text_summarizer
- Owner: himalayaashish
- Created: 2024-09-14T11:30:24.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2024-10-17T08:00:16.000Z (3 months ago)
- Last Synced: 2024-10-19T10:27:06.385Z (3 months ago)
- Topics: json-data, nlu, tensorflow2, transformer
- Language: Jupyter Notebook
- Homepage:
- Size: 49.8 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
## Hello World! 
🚀 Welcome to my git repo :Text Summarization
#####
#### Text Summarization Using Transformer Models
###### This project demonstrates how to leverage transformer-based models to perform text summarization on conversation data using the Hugging Face transformers library.A pre-trained BART-large-XSum model is utilized to summarize long conversations. The summarizer is optimized to run on a GPU, ensuring fast inference and efficient tokenization handling. The project showcases how to generate concise and meaningful summaries of dialogues.
###### This project summarizes conversations using the Hugging Face `transformers` library and the BART model for summarization. The model used here is `"lidiya/bart-large-xsum-samsum"`.
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## Installation
1. Clone this repository or download the script.
2. Install the required packages using pip:
```bash
pip install -r requirements.txt
```
```bash
python main.py
```
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