.gitignore

@@ -150,10 +150,6 @@ dmypy.json
 # pytype static type analyzer
 .pytype/
 
-# Model
-VeraMind-Mini
-VeraMind-Edge
-
 # Cython debug symbols
 cython_debug/
 

README.md

@@ -1,38 +1,44 @@
 # 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.
 
+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
 
-- **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](https://gitea.fabelous.app/Fabel/Fabelous-albert-uncased), for robust and reliable predictions. `VeraMind-Mini` is based on [DistilBERT](https://huggingface.co/distilbert/distilbert-base-multilingual-cased) and optimized for server-side usage.
+- 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.
 
+## Installation
 
-## Model Variants
+1. Clone this repository:
 
-- **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.
+```bash
+git clone https://github.com/yourusername/VeraMind.git
+cd VeraMind
+```
 
+2. Install the required dependencies:
 
-## Downloading the Model
+```bash
+pip install -r requirements.txt
+```
 
-You can download the `VeraMind` models from the following links:
+## Usage
 
-- [Download VeraMind-Mini (Server-Side)](https://gitea.fabelous.app/Fabel/VeraMind/releases/download/0.1/VeraMind-Mini.zip)
-- [Download VeraMind-Edge (Client-Side)](https://gitea.fabelous.app/Fabel/VeraMind/releases/download/0.1.1/VeraMind-Edge.zip)
+### Predicting News Authenticity
 
-
-## Usage Example
-
-The example below demonstrates how to use the `VeraMindInference` class to evaluate the authenticity of a news article:
+Here's how you can use the model to predict if a news article is real or fake:
 
 ```python
 from src.Inference import VeraMindInference
 
 # Load the model
-model = VeraMindInference("VeraMind-Edge")  # Use VeraMind-Mini for server-side usage
+model = VeraMindInference("path/to/VeraMind-Mini")
 
 # Example news article text
 text = "This is an example News Article"
@@ -43,35 +49,24 @@ result = model.predict(text)
 print(result)
 ```
 
-Output:
+The output will be a dictionary containing the result ("REAL" or "FAKE") and the confidence score:
 
 ```python
 {'result': 'FAKE', 'confidence': 0.9990140199661255}
 ```
 
+## Model Architecture
 
-## 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.
-
+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 warranties. While the model strives for accuracy, it may make mistakes. Always verify predictions by consulting multiple reliable sources. Use this tool responsibly.
+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.
 
+Always remember that machine learning models can make mistakes, so use this tool responsibly and critically evaluate its predictions.
 
-## License
+## Citation
 
-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.
+If you use this model in your research, please cite it as follows:
 
+> **VeraMind News Authenticity Checker** (2024). Retrieved from https://gitea.fabelous.app/Fabel/VeraMind by Falko Habel

main.py

@@ -2,7 +2,7 @@ from src.Inference import VeraMindInference
 
 
 # load model
-model = VeraMindInference("VeraMind-Edge")
+model = VeraMindInference("path/to/VeraMind-Mini")
 
 
 text = "This is a example News Article"
@@ -11,5 +11,5 @@ text = "This is a example News Article"
 result = model.predict(text)
 
 # Example Output
-# {'result': 'FAKE', 'confidence': 0.9981970191001892}
+# {'result': 'FAKE', 'confidence': 0.9990140199661255}
 print(result)