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README.md

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+# BERT-Base-Uncased Quantized Model for social media sentiment analysis
+
+This repository hosts a quantized version of the **bert-base-uncased** model, fine-tuned for social media sentiment analysis 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:** Social Media Sentiment Analysis
+- **Dataset:** Social Media Sentiments Analysis Dataset [Kaggle]  
+- **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
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+# Load quantized model
+model_name = "AventIQ-AI/bert-social-media-sentiment-analysis"
+model = BertForSequenceClassification.from_pretrained(model_name).to(device)
+tokenizer = BertTokenizer.from_pretrained(model_name)
+
+#Function to make analysis
+def predict_sentiment(text):
+    # Tokenize input text
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+    
+    # Move tensors to GPU if available
+    inputs = {key: val.to(device) for key, val in inputs.items()}
+
+    # Get model prediction
+    with torch.no_grad():
+        outputs = model(**inputs)
+    
+    # Get predicted class
+    logits = outputs.logits
+    predicted_class = torch.argmax(logits, dim=1).item()
+
+    # Map back to sentiment labels
+    sentiment_labels = {0: "Negative", 1: "Neutral", 2: "Positive"}
+    return sentiment_labels[predicted_class]
+
+# Define a test sentence
+test_sentence = "Spending time with family always brings me so much joy."
+print(f"Predicted Sentiment: {predict_sentiment(text)}")
+```
+
+## Performance Metrics
+
+- **Accuracy:** 0.82
+- **Precision:** 0.68
+- **Recall:** 0.82
+- **F1 Score:** 0.73
+
+## Fine-Tuning Details
+
+### Dataset
+
+The dataset is taken from Kaggle Social Media Sentiment Analysis.
+
+### Training
+
+- Number of epochs: 6  
+- Batch size: 8  
+- Evaluation strategy: epoch  
+- Learning rate: 3e-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.
+