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.
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.
