README.md

metadata

title: ArabGuard Egyptian
emoji: 🛡️
colorFrom: green
colorTo: blue
sdk: docker
app_port: 7860
pinned: false

ArabGuard Egyptian

This Docker Space trains the classifier during the image build from the public d12o6aa/ArabGuard-Egyptian-V1 dataset, then serves the trained model through Streamlit.

Only the source code, dependency lock, and Docker configuration belong in the Space repository. Model weights, checkpoints, downloaded datasets, caches, and dashboard outputs are generated during the Docker build and are ignored by Git.

The first build can take a long time because xlm-roberta-base is trained on CPU. The Dockerfile uses a separate training stage, so downloaded data, Hugging Face caches, optimizer states, and checkpoints are not copied into the runtime image. Changing only app.py reuses the cached training layer when Docker cache is available; changing train_model.py, requirements.txt, or an earlier layer starts training again.

Space repository contents

Upload only:

• .dockerignore
• .gitignore
• Dockerfile
• README.md
• app.py
• requirements.txt
• train_model.py

Do not upload .venv, model weights, checkpoints, dataset files, caches, or dashboard_data.

Before pushing, check the staged file list and sizes:

git status --short
git ls-files

The Space listens on port 7860, as required by the app_port metadata above.