# RoBERTa-Base Quantized Model for Topic Classification

This repository hosts a quantized version of the RoBERTa model, fine-tuned for topic classification using the AG News dataset. The model has been optimized using FP16 quantization for efficient deployment without significant accuracy loss.
## Model Details

- **Model Architecture:** RoBERTa Base  
- **Task:** Multi-class Topic Classification (4 classes)  
- **Dataset:** AG News (Hugging Face Datasets)  
- **Quantization:** Float16  
- **Fine-tuning Framework:** Hugging Face Transformers  

---

## Installation

```bash
pip install transformers torch datasets 
```

---

## Loading the Model

```python

from transformers import RobertaTokenizer
from transformers import RobertaForSequenceClassification

import torch

# Load tokenizer and model

tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
model = RobertaForSequenceClassification.from_pretrained("roberta-base", num_labels=4).to(device)
# Define test sentences
samples = [
    "Tensions rise in the Middle East as diplomats gather for emergency talks to prevent further escalation.",
"Tesla reports a 25% increase in quarterly revenue, driven by strong demand for its Model Y vehicles in Asia.",

"Researchers develop a new quantum computing chip that significantly reduces energy consumption.",
    "Argentina defeats Brazil 2-1 in the Copa América final, securing their 16th continental title.",
    "Meta unveils its latest AI model capable of generating 3D virtual environments from text prompts."
]



from transformers import pipeline

# Load pipeline for inference
classifier = pipeline("text-classification", model=trainer.model, tokenizer=tokenizer, device=0)  # device=-1 if using CPU

predictions = classifier(samples)

# Print results
for text, pred in zip(samples, predictions):
    print(f"\nText: {text}\nPredicted Topic: {pred['label']} (Score: {pred['score']:.4f})")
```

---

## Performance Metrics

- **Accuracy:** 0.9471  
- **Precision:** 0.9471  
- **Recall:** 0.9471  
- **F1 Score:** 0.9471 

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## Fine-Tuning Details

### Dataset

The dataset is sourced from Hugging Face’s ag_news dataset. It contains 120,000 training samples and 7,600 test samples, with each news article labeled into one of four categories: World, Sports, Business, or Sci/Tech. The original dataset was used as provided, and input texts were tokenized using the RoBERTa tokenizer and truncated/padded to a maximum length of 128 tokens.

### 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.

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## Repository Structure

```python
.
├── config.json                   # Model configuration
├── merges.txt                   # Byte Pair Encoding (BPE) merge rules for tokenizer
├── model.safetensors            # Quantized model weights
├── README.md                    # Model documentation
├── special_tokens_map.json      # Tokenizer special tokens
├── tokenizer_config.json        # Tokenizer configuration
├── vocab.json                   # Tokenizer vocabulary

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

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## Limitations

- The model is trained specifically for binary topic classification on ag news dataset.
- FP16 quantization may result in slight numerical instability in edge cases.


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## Contributing

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