Create README.md

README.md

@@ -0,0 +1,92 @@
+# 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.
+``
+