VeraMind

VeraMind is a fine-tuned machine learning model designed to predict whether a news article is real or fake. Built using the Hugging Face Transformers library and PyTorch, the VeraMind model suite is optimized for binary text classification tasks.

Features

• Real or Fake Prediction: Classifies news articles as "REAL" or "FAKE."
• Confidence Score: Provides a numerical confidence score for each prediction.
• Fine-Tuned Model: Uses VeraMind-Edge, a fine-tuned version of fabelous-albert-uncased, for robust and reliable predictions. VeraMind-Mini is based on DistilBERT and optimized for server-side usage.

Model Variants

• VeraMind-Mini: Optimized for server-side usage, offering robust performance and efficiency for large-scale operations. Based on DistilBERT architecture.
• VeraMind-Edge: Designed for client-side usage, offering a lightweight and resource-efficient solution for local inference. Based on fabelous-albert-uncased architecture.

Downloading the Model

You can download the VeraMind models from the following links:

• Download VeraMind-Mini (Server-Side)
• Download VeraMind-Edge (Client-Side)

Usage Example

The example below demonstrates how to use the VeraMindInference class to evaluate the authenticity of a news article:

from src.Inference import VeraMindInference

# Load the model
model = VeraMindInference("VeraMind-Edge")  # Use VeraMind-Mini for server-side usage

# Example news article text
text = "This is an example News Article"

# Predict if the news is real or fake
result = model.predict(text)

print(result)

Output:

{'result': 'FAKE', 'confidence': 0.9990140199661255}

Changelog

Current Version

• Model Architecture: Switched from DistilBERT to ALBERT architecture for VeraMind-Edge.
• Memory Usage: Reduced memory usage to approximately 10% of the previous version.
• Inference Speed: Slightly slower inference speed due to architectural changes.

Previous Version

• Used DistilBERT architecture with higher memory requirements and faster inference speed.

Disclaimer

This project is provided "as-is" without any warranties. While the model strives for accuracy, it may make mistakes. Always verify predictions by consulting multiple reliable sources. Use this tool responsibly.

License

This project is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0). You may use and share this software privately, but must credit the authors, refrain from commercial use, and avoid creating derivative works.

Feedback and Support

If you encounter any issues or have questions, feel free to reach out through the project's Gitea Issues page or contact our support team at support@fabelous.app.