corrected release links

.gitignore

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

README.md

@@ -1,44 +1,38 @@
 # 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
 
-- Predicts if a given news article is real or fake.
+- **Real or Fake Prediction**: Classifies news articles as "REAL" or "FAKE."
-- Provides a confidence score for the prediction.
+- **Confidence Score**: Provides a numerical confidence score for each prediction.
-- Utilizes the Hugging Face Transformers library for easy integration with other NLP models.
+- **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.
 
-## Installation
 
-1. Clone this repository:
+## Model Variants
 
-```bash
+- **VeraMind-Mini**: Optimized for server-side usage, offering robust performance and efficiency for large-scale operations. Based on DistilBERT architecture.
-git clone https://github.com/yourusername/VeraMind.git
+- **VeraMind-Edge**: Designed for client-side usage, offering a lightweight and resource-efficient solution for local inference. Based on fabelous-albert-uncased architecture.
-cd VeraMind
-```
 
-2. Install the required dependencies:
 
-```bash
+## Downloading the Model
-pip install -r requirements.txt
-```
 
-## Usage
+You can download the `VeraMind` models from the following links:
 
-### Predicting News Authenticity
+- [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)
 
-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
 
 # Load the model
-model = VeraMindInference("path/to/VeraMind-Mini")
+model = VeraMindInference("VeraMind-Edge")  # Use VeraMind-Mini for server-side usage
 
 # Example news article text
 text = "This is an example News Article"
@@ -49,24 +43,35 @@ result = model.predict(text)
 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.
+## 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 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
+## License
 
-If you use this model in your research, please cite it as follows:
+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.
 
-> **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("path/to/VeraMind-Mini")
+model = VeraMindInference("VeraMind-Edge")
 
 
 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': 0.9990140199661255}
+# {'result': 'FAKE', 'confidence': 0.9981970191001892}
 print(result)