Deduplicate educational disclaimer text

MODEL_README.md

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 # Spam Email Classifier — sklearn Voting Ensemble with XAI (v2)
 
 **ENGT 375 — Applied Machine Learning | Spring 2026 | Old Dominion University**
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-> **Disclaimer:** This model was created as a student project for Applied Machine Learning at ODU. It is intended for **educational and research purposes only** and should not be used as a sole spam/phishing filter in production. Classification accuracy may vary, and the model may produce incorrect or misleading results. Always use established email security tools for real-world spam filtering.
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 A voting ensemble classifier (Random Forest + Logistic Regression + Linear SVM with calibration) for spam email detection, with LIME, SHAP, and ELI5 explainability support. This is the **v2** model — a beginner-friendly course-facing rewrite of the original `spam-xai-project`, retrained on the full 99,999-sample corpus. The Gradio workflow was previously merged in from the now-retired `spam-classifier-gradio` project.
 
 ## Model Details

README.md

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 # Spam Email Classifier with XAI Explanations
 
 **ENGT 375 — Applied Machine Learning | Spring 2026 | Old Dominion University**
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-> **Disclaimer:** This model was created as a student project for Applied Machine Learning at ODU. It is intended for **educational and research purposes only** and should not be used as a sole spam/phishing filter in production. Classification accuracy may vary, and the model may produce incorrect or misleading results. Always use established email security tools for real-world spam filtering.
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 A Gradio web app that classifies emails as spam or ham and provides explainable AI (XAI) insights using three different methods (LIME, SHAP, and ELI5).
 
 **What's new in v2:** beginner-friendly notebook refactor (explicit for-loops over comprehensions, no decorators, no premature abstractions), lecture-style charts in the student teaching notebook, a separate `app_student.py` / `utils_student.py` / `retrain_student.py` track for course readers, and a fresh full-dataset retrain (99,999 samples — 69,999 train / 30,000 test) producing a re-tuned classification threshold of 0.3714. v2 is deployed as its own HuggingFace Space at `VoltageVagabond/spam-xai-classifier-v2`.