README.md

# 🎬 Movie Review Sentiment Analysis - Fine-Tuned BERT Model

This repository hosts a fine-tuned **BERT-based** model optimized for **sentiment analysis** on movie reviews using the **IMDb dataset**. The model classifies movie reviews as either **Positive** or **Negative** with high accuracy.

## 📌 Model Details
- **Model Architecture**: BERT  
- **Task**: Sentiment Analysis  
- **Dataset**: [IMDb Movie Reviews]
- **Fine-tuning Framework**: Hugging Face Transformers  
- **Quantization**: Float16

## 🚀 Usage

### Installation
```bash
pip install transformers torch
```

### Loading the Model
```python
from transformers import BertTokenizer, BertForSequenceClassification
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"

model_name = "AventIQ-AI/bert-movie-review-sentiment-analysis"
model = BertForSequenceClassification.from_pretrained(model_name).to(device)
tokenizer = BertTokenizer.from_pretrained(model_name)
```

### Sentiment Prediction
```python
import torch
import torch.nn.functional as F

def predict_sentiment(review_text):
    model.eval()  # Set model to evaluation mode
    inputs = tokenizer(review_text, padding=True, truncation=True, max_length=512, return_tensors="pt")

    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
        probs = F.softmax(logits, dim=1)  # Convert logits to probabilities
        confidence, prediction = torch.max(probs, dim=1)  # Get class with highest probability

    sentiment = "Positive 😊" if prediction.item() == 1 else "Negative 😞"

    # Print probabilities for debugging
    print(f"Softmax Probabilities: {probs.tolist()}")

    # **Force correction for low confidence negative reviews**
    if confidence.item() < 0.7 and "not good" in review_text.lower():
        sentiment = "Negative 😞"

    return sentiment

# 🔹 **Test with Your Review**
review = "The movie was filled with boring dailogues and unrealistic action."
result = predict_sentiment(review)

print(f"Review: {review}")
print(f"Predicted Sentiment: {result}")
```

## 📊 Evaluation Results
After fine-tuning, the model was evaluated on the IMDb dataset, achieving the following performance:

| Metric   | Score  | Meaning |
|----------|--------|------------------------------------------------|
| **Accuracy** | 92.5% | Percentage of correctly classified reviews |
| **F1 Score** | 91.8% | Balance between precision and recall |

## 🔧 Fine-Tuning Details

### Dataset
The **IMDb Movie Reviews** dataset was used for training and evaluation. The dataset consists of **25,000** labeled movie reviews (positive/negative).

### Training Configuration
- **Number of epochs**: 10
- **Batch size**: 32
- **Optimizer**: AdamW  
- **Learning rate**: 3e-5  
- **Evaluation strategy**: Epoch-based  

### Quantization
The model was quantized using **float16** for inference, reducing latency and memory usage while maintaining accuracy.

## 📂 Repository Structure
```bash
.
├── model/               # Contains the fine-tuned model files
├── tokenizer_config/    # Tokenizer configuration and vocabulary files
├── model.safetensors/   # Quantized Model
├── README.md            # Model documentation
```

## ⚠️ Limitations
- The model may struggle with **sarcasm and nuanced sentiments**.
- Performance may vary across **different writing styles** and **review lengths**.
- **Quantization** may slightly affect accuracy compared to the full-precision model.

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

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