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Text-summarization-on-Reddit-posts-using-t5-small

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# t5-small Quantized Model for Text Summarization on Reddit-TIFU dataset

This repository hosts a quantized version of the t5-small model, fine-tuned for text summarization using the Reddit-TIFU dataset. The model has been optimized using FP16 quantization for efficient deployment without significant accuracy loss.

## Model Details

- **Model Architecture:** t5-small(short version)  
- **Task:** Text generation  
- **Dataset:** Reddit-TIFU (Hugging Face Datasets)  
- **Quantization:** Float16  
- **Fine-tuning Framework:** Hugging Face Transformers  

---

## Installation

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

---

## Loading the Model

```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import torch

# Load tokenizer and model
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model_name = "t5-small"  
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(device)

# Define test sentences
new_text = """
Today I was late to my morning meeting because I spilled coffee all over my laptop. 
Then I realized my backup laptop was also out of battery. 
Eventually joined from my phone, only to find out the meeting was cancelled.
"""

# Generate 
def generate_summary(text):
    inputs = tokenizer(
        text,
        return_tensors="pt",
        max_length=512,
        truncation=True
    ).to(device)

    summary_ids = model.generate(
        inputs["input_ids"],
        max_length=100,
        min_length=5,
        num_beams=4,
        length_penalty=2.0,
        early_stopping=True
    )

    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
    return summary
```

---

## Performance Metrics

- **Rouge1:** 19.590 
- **Rouge2:** 4.270  
- **Rougel:** 16.390  
- **Rougelsum:** 16.800  

---

## 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

- **Epochs:** 3  
- **Batch size:** 8  
- **Learning rate:** 2e-5  
- **Evaluation strategy:** `epoch`  

---

## Quantization

Post-training quantization was applied using PyTorch’s `half()` precision (FP16) to reduce model size and inference time.

---

## Repository Structure

```python
.
├── quantized-model/               # Contains the quantized model files
│   ├── config.json
│   ├── model.safetensors
│   ├── tokenizer_config.json
│   ├── spiece.model
│   └── special_tokens_map.json
│   ├── generation_config.jason
│   └── tokenizer.json

├── README.md                      # Model documentation
```

---

## Limitations

- The model is trained specifically for text summarization on reddit posts
- FP16 quantization may result in slight numerical instability in edge cases.


---

## Contributing

Feel free to open issues or submit pull requests to improve the model or documentation.