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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.
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