updated readme for new edge release
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# pytype static type analyzer
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.pytype/
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# Model
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VeraMind-Mini
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VeraMind-Edge
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# Cython debug symbols
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cython_debug/
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33
README.md
33
README.md
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# VeraMind
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**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.
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**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.
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## Features
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- **Real or Fake Prediction**: Classifies news articles as "REAL" or "FAKE."
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- **Confidence Score**: Provides a numerical confidence score for each prediction.
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- **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.
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- **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.
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## Model Variants
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- **VeraMind-Mini**: Optimized for server-side usage, offering robust performance and efficiency for large-scale operations. Based on DistilBERT architecture.
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- **VeraMind-Edge**: Designed for client-side usage, offering a lightweight and resource-efficient solution for local inference. Based on fabelous-albert-uncased architecture.
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## Downloading the Model
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You can download the `VeraMind-Mini` model from the following link:
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[Download VeraMind-Mini Model](https://gitea.fabelous.app/Fabel/VeraMind/releases/download/latest/VeraMind-Mini.zip)
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You can download the `VeraMind` models from the following links:
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- [Download VeraMind-Mini (Server-Side)](https://gitea.fabelous.app/Fabel/VeraMind/releases/download/latest/VeraMind-Mini.zip)
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- [Download VeraMind-Edge (Client-Side)](https://gitea.fabelous.app/Fabel/VeraMind/releases/download/latest/VeraMind-Edge.zip)
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## Usage Example
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@ -28,7 +32,7 @@ The example below demonstrates how to use the `VeraMindInference` class to evalu
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from src.Inference import VeraMindInference
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# Load the model
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model = VeraMindInference("path/to/VeraMind-Mini")
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model = VeraMindInference("VeraMind-Edge") # Use VeraMind-Mini for server-side usage
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# Example news article text
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text = "This is an example News Article"
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```
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## Changelog
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### Current Version
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- **Model Architecture**: Switched from DistilBERT to ALBERT architecture for VeraMind-Edge.
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- **Memory Usage**: Reduced memory usage to approximately 10% of the previous version.
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- **Inference Speed**: Slightly slower inference speed due to architectural changes.
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### Previous Version
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- Used DistilBERT architecture with higher memory requirements and faster inference speed.
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## Disclaimer
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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.
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## License
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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.
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## Feedback and Support
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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.
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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.
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4
main.py
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main.py
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@ -2,7 +2,7 @@ from src.Inference import VeraMindInference
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# load model
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model = VeraMindInference("path/to/VeraMind-Mini")
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model = VeraMindInference("VeraMind-Edge")
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text = "This is a example News Article"
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result = model.predict(text)
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# Example Output
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# {'result': 'FAKE', 'confidence': 0.9990140199661255}
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# {'result': 'FAKE', 'confidence': 0.9981970191001892}
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print(result)
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