fzn0x
/

bert-spam-classification-model

@@ -1,199 +1,100 @@
----
-library_name: transformers
-tags: []
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+# Fine-tuned BERT-base-uncased pre-trained model to classify spam SMS.
+Github: https://github.com/fzn0x/bert-sms-classification
+My second project in Natural Language Processing (NLP), where I fine-tuned a bert-base-uncased model to classify spam SMS. This is huge improvements from https://github.com/fzn0x/bert-indonesian-english-hate-comments.
+How to use this model?
+```
+model = BertForSequenceClassification.from_pretrained('fzn0x/bert-spam-classification-model')
+```
+## ✅ Install requirements
+Install required dependencies
+```sh
+pip install --upgrade pip
+pip install -r requirements.txt
+```
+## ✅ Add BERT virtual env
+write the command below
+```sh
+# ✅ Create and activate a virtual environment
+python -m venv bert-env
+source bert-env/bin/activate    # On Windows use: bert-env\Scripts\activate
+```
+## ✅ Install CUDA
+Check if your GPU supports CUDA:
+```sh
+nvidia-smi
+```
+Then:
+```sh
+pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
+PYTORCH_CUDA_ALLOC_CONF=expandable_segments:False
+```
+## 🔧 How to use
+- Check your device and CUDA availability:
+```sh
+python check_device.py
+```
+> :warning: Using CPU is not advisable, prefer check your CUDA availability.
+- Train the model:
+```sh
+python scripts/train.py
+```
+> :warning: Remove unneeded checkpoint in models/pretrained to save your storage after training
+- Run prediction:
+```sh
+python scripts/predict.py
+```
+✅ Dataset Location: [`data/spam.csv`](./data/spam.csv), modify the dataset to enhance the model based on your needs.
+## 📚 Citations
+If you use this repository or its ideas, please cite the following:
+See [`citations.bib`](./citations.bib) for full BibTeX entries.
+- Wolf et al., *Transformers: State-of-the-Art Natural Language Processing*, EMNLP 2020. [ACL Anthology](https://www.aclweb.org/anthology/2020.emnlp-demos.6)
+- Pedregosa et al., *Scikit-learn: Machine Learning in Python*, JMLR 2011.
+- Almeida & Gómez Hidalgo, *SMS Spam Collection v.1*, UCI Machine Learning Repository (2011). [Kaggle Link](https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset)
+## 🧠 Credits and Libraries Used
+- [Hugging Face Transformers](https://github.com/huggingface/transformers) – model, tokenizer, and training utilities
+- [scikit-learn](https://scikit-learn.org/stable/) – metrics and preprocessing
+- Logging silencing inspired by Hugging Face GitHub discussions
+- Dataset from [UCI SMS Spam Collection](https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset)
+- Inspiration from [Kaggle Notebook by Suyash Khare](https://www.kaggle.com/code/suyashkhare/naive-bayes)
+## License and Usage
+License under [MIT license](./LICENSE).
+---
+Leave a ⭐ if you think this project is helpful, contributions are welcome.
+---