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README.md

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+# BERT-Base-Uncased Quantized Model for Sentiment Analysis for Ad Campaign Performance
+
+This repository hosts a quantized version of the BERT model, fine-tuned for stock-market-analysis-sentiment-classification tasks. The model has been optimized for efficient deployment while maintaining high accuracy, making it suitable for resource-constrained environments.
+
+## Model Details
+
+- **Model Architecture:** BERT Base Uncased  
+- **Task:** Sentiment Analysis for Ad Campaign Performance
+- **Dataset:** Stanford Sentiment Treebank v2 (SST2)  
+- **Quantization:** Float16  
+- **Fine-tuning Framework:** Hugging Face Transformers  
+
+## Usage
+
+### Installation
+
+```sh
+pip install transformers torch
+```
+
+
+### Loading the Model
+
+```python
+
+from transformers import BertForSequenceClassification, BertTokenizer
+import torch
+
+# Load quantized model
+quantized_model_path = "AventIQ-AI/sentiment-analysis-for-ad-campaign-performance"
+quantized_model = BertForSequenceClassification.from_pretrained(quantized_model_path)
+quantized_model.eval()  # Set to evaluation mode
+quantized_model.half()  # Convert model to FP16
+
+# Load tokenizer
+tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
+
+# Define a test sentence
+test_sentence = "The new summer ad campaign was a hit on social media! Customers loved the vibrant visuals and catchy slogan. However, some viewers felt the message was a bit too generic and didn't connect with the brand's core identity. Overall, there was a positive buzz, with many comments praising the ad’s creativity and fun vibe."
+
+# Tokenize input
+inputs = tokenizer(test_sentence, return_tensors="pt", padding=True, truncation=True, max_length=128)
+
+# Ensure input tensors are in correct dtype
+inputs["input_ids"] = inputs["input_ids"].long()  # Convert to long type
+inputs["attention_mask"] = inputs["attention_mask"].long()  # Convert to long type
+
+# Make prediction
+with torch.no_grad():
+    outputs = quantized_model(**inputs)
+
+# Get predicted class
+predicted_class = torch.argmax(outputs.logits, dim=1).item()
+print(f"Predicted Class: {predicted_class}")
+
+
+label_mapping = {0: "very_negative", 1: "nagative", 2: "neutral", 3: "Positive", 4: "very_positive"}  # Example
+
+predicted_label = label_mapping[predicted_class]
+print(f"Predicted Label: {predicted_label}")
+
+```
+
+## Performance Metrics
+
+- **Accuracy:** 0.82 
+
+## Fine-Tuning Details
+
+### Dataset
+
+The dataset is taken from Kaggle Stanford Sentiment Treebank v2 (SST2).
+
+### Training
+
+- Number of epochs: 3  
+- Batch size: 8  
+- Evaluation strategy: epoch  
+- Learning rate: 2e-5  
+
+### Quantization
+
+Post-training quantization was applied using PyTorch's built-in quantization framework to reduce the model size and improve inference efficiency.
+
+## Repository Structure
+
+```
+.
+├── model/               # Contains the quantized model files
+├── tokenizer_config/    # Tokenizer configuration and vocabulary files
+├── model.safensors/     # Fine Tuned Model
+├── README.md            # Model documentation
+```
+
+## Limitations
+
+- The model may not generalize well to domains outside the fine-tuning dataset.  
+- Quantization may result in minor accuracy degradation compared to full-precision models.  
+
+## Contributing
+
+Contributions are welcome! Feel free to open an issue or submit a pull request if you have suggestions or improvements.