# BERT-Base-Uncased Quantized Model for Twitter Tweet Sentiment Classification

This repository hosts a quantized version of the **T5-Base** model, fine-tuned for **Movie Script Writting**. The model is optimized for efficient deployment while maintaining high accuracy, making it suitable for resource-constrained environments such as mobile and edge devices.

## Model Details

- **Model Architecture:** T5-Base  
- **Task:** Movie Script Writting 
- **Dataset:** bookcorpus 
- **Quantization:** Float16 (FP16)  
- **Fine-tuning Framework:** Hugging Face Transformers  
- **Inference Framework:** PyTorch  

## Usage

### Installation

```sh
pip install transformers torch
```

### Loading the Model

```python
from transformers import BertForSequenceClassification, BertTokenizer
import torch

# Load quantized model
quantized_model_path = "path/to/bert_finetuned_fp16"


def generate_script(prompt):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")  # Check available device
    model.to(device)  # Move model to the appropriate device
    
    inputs = tokenizer(f"Generate a movie script: {prompt}", return_tensors="pt", truncation=True, padding="max_length", max_length=256)
    inputs = {key: value.to(device) for key, value in inputs.items()}  # Move inputs to same device as model

    with torch.no_grad():
        outputs = model.generate(**inputs, max_length=256, num_return_sequences=1)

    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Test the script generator
prompt = "SCENE: EXT. DARK ALLEY - NIGHT"
print(generate_script(prompt))


## Performance Metrics

- **Accuracy:** 0.82  
- **Inference Speed:** Faster due to FP16 quantization  

## Fine-Tuning Details

### Dataset



### Training Configuration

- **Number of epochs:** 3  
- **Batch size:** 8  
- **Evaluation strategy:** Per epoch  
- **Learning rate:** 2e-5  
- **Optimizer:** AdamW  

### Quantization

The model is quantized using **Post-Training Quantization (PTQ)** with **Float16 (FP16)**, which reduces model size and improves inference efficiency while maintaining accuracy.

## Repository Structure

```
.
├── model/               # Contains the quantized model files
├── tokenizer_config/    # Tokenizer configuration and vocabulary files
├── model.safensors/     # Fine-tuned and quantized model
├── README.md            # Model documentation
```

## Limitations

- The model is optimized for English-language next-word prediction tasks.
- While quantization improves speed, minor accuracy degradation may occur.
- Performance on out-of-distribution text (e.g., highly technical or domain-specific data) may be limited.

## Contributing

Contributions are welcome! Feel free to open an issue or submit a pull request if you have suggestions or improvements.
``