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README.md

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+# DistilBERT Base Uncased Quantized Model for Sentiment Analysis
+
+This repository hosts a quantized version of the DistilBERT model, fine-tuned for 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:** DistilBERT Base Uncased  
+- **Task:** Sentiment Analysis  
+- **Dataset:** IMDB Reviews  
+- **Quantization:** Float16  
+- **Fine-tuning Framework:** Hugging Face Transformers  
+
+## Usage
+
+### Installation
+
+```sh
+pip install transformers torch
+```
+
+### Loading the Model
+
+```python
+from transformers import DistilBertTokenizer, DistilBertForSequenceClassification, Trainer, TrainingArguments
+import torch
+
+model_name = "AventIQ-AI/distilbert-base-uncased-sentiment-analysis"
+tokenizer = DistilBertTokenizer.from_pretrained(model_name)
+model = DistilBertForSequenceClassification.from_pretrained(model_name)
+
+def predict_sentiment(text):
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
+    with torch.no_grad():
+        logits = model(**inputs).logits
+    predicted_class_id = torch.argmax(logits, dim=-1).item()
+    return "Positive" if predicted_class_id == 1 else "Negative"
+
+# Test the model with a sample sentence
+test_text = "I absolutely loved the movie! It was fantastic."
+print(f"Sentiment: {predict_sentiment(test_text)}")
+```
+
+## Performance Metrics
+
+- **Accuracy:** 0.56  
+- **F1 Score:** 0.56  
+- **Precision:** 0.68  
+- **Recall:** 0.56  
+
+## Fine-Tuning Details
+
+### Dataset
+
+The IMDb Reviews dataset was used, containing both positive and negative sentiment examples.
+
+### Training
+
+- Number of epochs: 3  
+- Batch size: 16  
+- 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.
+