# BART-Based Text Summarization Model for News Aggregation

This repository hosts a BART transformer model fine-tuned for abstractive text summarization of news articles. It is designed to condense lengthy news reports into concise, informative summaries, enhancing user experience for news readers and aggregators.

## Model Details

- **Model Architecture:** BART (Facebook's BART-base)
- **Task:** Abstractive Text Summarization
- **Domain:** News Articles
- **Dataset:** Reddit-TIFU (Hugging Face Datasets)
- **Fine-tuning Framework:** Hugging Face Transformers

## Usage

### Installation

```bash
pip install datasets transformers rouge-score evaluate
```

### Loading the Model

```python
from transformers import BartTokenizer, BartForConditionalGeneration, Trainer, TrainingArguments, DataCollatorForSeq2Seq
import torch

# Load tokenizer and model
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model_name = "facebook/bart-base"  
tokenizer = BartTokenizer.from_pretrained(model_name)
model = BartForConditionalGeneration.from_pretrained(model_name).to(device)
```

## Performance Metrics

- **Rouge1 :** 25.500000
- **Rouge2 :** 7.860000
- **Rougel :** 20.640000
- **Rougelsum :** 21.180000


## Fine-Tuning Details

### Dataset

The dataset is sourced from Hugging Face’s Reddit-TIFU dataset. It contains 79,000 reddit post and their summaries.
The original training and testing sets were merged, shuffled, and re-split using an 90/10 ratio.

### Training Configuration

- **Epochs:** 3
- **Batch Size:** 8
- **Learning Rate:** 2e-5
- **Evaluation Strategy:** epoch

### Quantization
 
Post-training quantization was applied using PyTorch's built-in quantization framework to reduce the model size and improve inference efficiency.

## Repository Structure

```
.
├── config.json
├── tokenizer_config.json    
├── sepcial_tokens_map.json 
├── tokenizer.json        
├── model.safetensors    # Fine Tuned Model
├── README.md            # Model documentation
```

## Limitations
 
- The model may not generalize well to domains outside the fine-tuning dataset.  

- Quantization may result in minor accuracy degradation compared to full-precision models.  
 
## Contributing
 
Contributions are welcome! Feel free to open an issue or submit a pull request if you have suggestions or improvements.