https://github.com/shuyib/hf_model_preview
Using LLMs in huggingface for sentiment analysis, translation, summarization and extractive question answering
https://github.com/shuyib/hf_model_preview
decoder-model encoder-model explainable-ai extractive-question-answering facebook-bart helsinki-nlp llm llm-inference question-answering qwen2-5 sentiment-analysis summarization translation
Last synced: 4 months ago
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Using LLMs in huggingface for sentiment analysis, translation, summarization and extractive question answering
- Host: GitHub
- URL: https://github.com/shuyib/hf_model_preview
- Owner: Shuyib
- License: cc0-1.0
- Created: 2025-04-11T15:02:31.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-05-26T10:22:51.000Z (5 months ago)
- Last Synced: 2025-05-26T11:42:21.055Z (5 months ago)
- Topics: decoder-model, encoder-model, explainable-ai, extractive-question-answering, facebook-bart, helsinki-nlp, llm, llm-inference, question-answering, qwen2-5, sentiment-analysis, summarization, translation
- Language: Jupyter Notebook
- Homepage:
- Size: 155 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
[](https://mybinder.org/v2/gh/Shuyib/HF_model_preview/HEAD)
[](https://colab.research.google.com/github/Shuyib/HF_model_preview/blob/master/notebook.ipynb)
# Car-ing is Sharing: LLM Model Review
## Project Overview
This project evaluates various Hugging Face large language models (LLMs) for a car dealership chatbot application called "Car-ing is Sharing". The chatbot is designed to assist customers and provide support to human agents through multiple NLP functionalities.
### Key Features
- **Sentiment Analysis**: Classify car reviews as positive or negative
- **Text Translation**: Translate customer reviews between English and Spanish
- **Question Answering**: Extract specific information from car reviews
- **Text Summarization**: Generate concise summaries of longer car reviews
## Project Structure
```
.
├── car.jpeg # Project image
├── Makefile # Build automation
├── notebook.ipynb # Main analysis notebook
├── requirements.txt # Project dependencies
└── data/
├── car_reviews.csv # Car review dataset
└── reference_translations.txt # Translation reference data
```
## Installation
1. Clone the repository:
```bash
git clone https://github.com/Shuyib/HF_model_preview.git
cd HF_model_review
```
2. Create and activate a virtual environment:
```bash
python3 -m venv .venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate
```
3. Install dependencies:
```bash
pip install -r requirements.txt
```
## Models Used
This project leverages several pre-trained models:
- **Sentiment Analysis**:
- `distilbert-base-uncased-finetuned-sst-2-english`
- `Qwen/Qwen2.5-1.5B-Instruct` (via API)
- **Translation**:
- `Helsinki-NLP/opus-mt-en-es`
- **Question Answering**:
- `deepset/minilm-uncased-squad2`
- **Summarization**:
- `cnicu/t5-small-booksum`
- `facebook/bart-large-cnn`
## Usage
1. Open the Jupyter notebook:
```bash
jupyter notebook notebook.ipynb
```
2. Run the cells to see model evaluations for:
- Sentiment analysis with accuracy and F1 metrics
- Translation quality with BLEU score calculation
- Question answering capabilities
- Text summarization quality
## API Access
For some models, you'll need to set up API access from huggingface.co. You can do this by creating a token on your Hugging Face account and setting it as an environment variable:
```bash
export HF_token="your_huggingface_token"
```
```python
import os
os.environ["HF_TOKEN"] = "your_huggingface_token"
```
## Development
This project includes a comprehensive Makefile with useful commands:
- `make install`: Set up the environment
- `make format`: Format code
- `make lint`: Lint code
- `make test`: Run tests
- `make clean`: Clean up environment
Run `make help` to see all available commands.
## Requirements
- Python 3.10 or higher (Used 3.11.9)
- See requirements.txt for Python dependencies:
- transformers
- evaluate
- datasets
- sentencepiece
- openai
- tenacity
- ipykernel
- torch
## License
[Creative Common License v1.0 Universal](https://github.com/Shuyib/HF_model_preview/blob/main/LICENSE)
## Acknowledgments
- Hugging Face for providing the model infrastructure
- Datasets are from [Datacamp projects](https://app.datacamp.com/learn/projects/2046)
- Special thanks to the teams behind the pre-trained models used in this project such as the Helsinki-NLP, Qwen team, and Facebook teams.