README.md

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

pip install transformers torch

Loading the Model

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.