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README.md
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predict()
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```
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## ✅ Install requirements
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Install required dependencies
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```sh
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pip install --upgrade pip
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pip install -r requirements.txt
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```
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## ✅ Add BERT virtual env
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write the command below
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```sh
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# ✅ Create and activate a virtual environment
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python -m venv bert-env
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source bert-env/bin/activate # On Windows use: bert-env\Scripts\activate
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```
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## ✅ Install CUDA
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Check if your GPU supports CUDA:
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```sh
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nvidia-smi
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```
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Then:
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```sh
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pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
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PYTORCH_CUDA_ALLOC_CONF=expandable_segments:False
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```
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## 🔧 How to use
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- Check your device and CUDA availability:
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```sh
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python check_device.py
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```
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> :warning: Using CPU is not advisable, prefer check your CUDA availability.
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- Train the model:
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```sh
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python scripts/train.py
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```
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> :warning: Remove unneeded checkpoint in models/pretrained to save your storage after training
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- Run prediction:
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```sh
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python scripts/predict.py
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```
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✅ Dataset Location: [`data/spam.csv`](./data/spam.csv), modify the dataset to enhance the model based on your needs.
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## 📚 Citations
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If you use this repository or its ideas, please cite the following:
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- Logging silencing inspired by Hugging Face GitHub discussions
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- Dataset from [UCI SMS Spam Collection](https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset)
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- Inspiration from [Kaggle Notebook by Suyash Khare](https://www.kaggle.com/code/suyashkhare/naive-bayes)
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## License and Usage
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License under [MIT license](./LICENSE).
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---
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Leave a ⭐ if you think this project is helpful, contributions are welcome.
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---
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predict()
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```
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## 📚 Citations
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If you use this repository or its ideas, please cite the following:
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- Logging silencing inspired by Hugging Face GitHub discussions
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- Dataset from [UCI SMS Spam Collection](https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset)
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- Inspiration from [Kaggle Notebook by Suyash Khare](https://www.kaggle.com/code/suyashkhare/naive-bayes)
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