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# 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-Mini` model is optimized for binary text classification tasks.
The VeraMind is an open-source Python application built using the Hugging Face Transformers library and PyTorch. It leverages a pre-trained model (`VeraMind-Mini`) to predict whether a given news article is real or fake with a confidence score.
This project is licensed under the [Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)](https://creativecommons.org/licenses/by-nc-nd/4.0/) license. You are free to use and share this model privately, but you must give appropriate credit, not use it for commercial purposes, and not distribute derivative works.
**Note:** This is a machine learning model and may make mistakes. It should not replace your own critical thinking when evaluating news authenticity. Always verify information from multiple reliable sources.
## Features
- Predicts if a given news article is real or fake.
- Provides a confidence score for the prediction.
- Utilizes the Hugging Face Transformers library for easy integration with other NLP models.
- **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-Mini`, a fine-tuned version of [fabelous-albert-uncased](https://gitea.fabelous.app/Fabel/Fabelous-albert-uncased), for robust and reliable predictions.
## Installation
1. Clone this repository:
```bash
git clone https://github.com/yourusername/VeraMind.git
cd VeraMind
```
## Downloading the Model
2. Install the required dependencies:
You can download the `VeraMind-Mini` model from the following link:
```bash
pip install -r requirements.txt
```
[Download VeraMind-Mini Model](https://gitea.fabelous.app/Fabel/VeraMind/releases/download/latest/VeraMind-Mini.zip)
## Usage
### Predicting News Authenticity
Here's how you can use the model to predict if a news article is real or fake:
## Usage Example
The example below demonstrates how to use the `VeraMindInference` class to evaluate the authenticity of a news article:
```python
from src.Inference import VeraMindInference
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print(result)
```
The output will be a dictionary containing the result ("REAL" or "FAKE") and the confidence score:
Output:
```python
{'result': 'FAKE', 'confidence': 0.9990140199661255}
```
## Model Architecture
The `VeraMind-Mini` model used in this application is a fine-tuned version of the [DistilBERT](https://huggingface.co/distilbert-base-uncased) model for binary text classification. It's designed to distinguish between real and fake news articles.
## Disclaimer
This project is provided as-is, without any express or implied warranty. The maintainers are not responsible for any damages arising from the use of this software.
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.
Always remember that machine learning models can make mistakes, so use this tool responsibly and critically evaluate its predictions.
## Citation
If you use this model in your research, please cite it as follows:
## License
> **VeraMind News Authenticity Checker** (2024). Retrieved from https://gitea.fabelous.app/Fabel/VeraMind by Falko Habel
This project is licensed under the [Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)](https://creativecommons.org/licenses/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](https://gitea.fabelous.app/Fabel/Fabelous-albert-uncased/issues) or contact our support team at support@fabelous.app.