📩 SMS Spam Classifier

A simple spam detection project using PyTorch, Scikit-learn, and the Hugging Face sms_spam dataset. The model is trained to classify SMS messages as spam or not spam using a logistic regression-based neural network (CircleModelV0).

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📁 Project Structure

Sms-Spam/
├── main.ipynb          # Training notebook
├── inference.py        # Inference script
├── full_model.pth      # Trained PyTorch model (full)
├── vectorizer.pkl      # CountVectorizer object (for text preprocessing)
└── README.md

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🧠 Model Architecture (CircleModelV0)

A simple feed-forward neural network:

• Input Layer: 8713 features (from CountVectorizer)
• Hidden Layer: 64 neurons + ReLU
• Output Layer: 1 neuron (sigmoid for binary classification)

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🚀 Getting Started

1. Clone the repository

git clone https://github.com/milliyin/sms-spam-model-train.git
cd Sms-Spam

2. Install dependencies

pip install torch scikit-learn datasets huggingface_hub

3. Run Inference

python inference.py

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🧪 Example Output

Input: you have won a free prize
Output: Spam

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🗃️ Dataset

The model uses the public dataset ucirvine/sms_spam from Hugging Face. It contains 5,574 SMS messages labeled as spam or ham.

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🧠 My Learning Journey

This project is part of my learning process on how to train machine learning models using my own code. Here’s what I’ve learned and implemented:

• ✅ Installed and configured libraries like torch, scikit-learn, and datasets.
• ✅ Downloaded and loaded the dataset using the Hugging Face Datasets library.
• ✅ Preprocessed the raw SMS data using CountVectorizer to convert text into numerical feature vectors.
• ✅ Converted those vectors to PyTorch tensors.
• ✅ Split the data into training and testing sets using train_test_split.
• ✅ Assigned input features (X: SMS prompts) and labels (y: 0 for ham, 1 for spam).
• ✅ Created a custom model class CircleModelV0 using PyTorch's nn.Module.
• ✅ Wrote the training loop including forward pass, loss computation, backpropagation, and optimization.
• ✅ Evaluated the model using accuracy and tested it on new inputs.
• ✅ Saved and reloaded the model and vectorizer for inference.

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🔐 Notes on PyTorch 2.6+

If you're using PyTorch 2.6+, the torch.load() call needs to allow loading full models with custom classes:

model = torch.load("full_model.pth", weights_only=False)

Make sure the class CircleModelV0 is defined or imported in your script before calling torch.load.

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📬 Contact

Feel free to open issues or suggest improvements!
Made with ❤️ for NLP enthusiasts.