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README.md

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+---
+license: mit
+tags: [xaitalk, tabular, explainable-ai, heart-disease]
+---
+
+# MLP — UCI Heart Disease (Cleveland) — xaitalk tabular demo
+
+A small 4-layer MLP (13→64→32→16→2) trained once on the **UCI Heart Disease
+(Cleveland)** dataset, frozen and published so xaitalk's tabular adapter loads
+**fixed, reproducible weights** (via `xaitalk.hub.ensure_model`) across
+PyTorch / TensorFlow / JAX — rather than retraining on every `load()`.
+
+This makes the tabular domain deterministic (canonical + cross-device
+consistency) and, crucially, demonstrates XAI on **real, interpretable
+clinical features** (age, cholesterol, resting BP, ...) rather than synthetic
+data — there is no signal to attribute in random data.
+
+- Weights: framework-agnostic numpy `.npz` (JAX layout; PT transposes Dense).
+- Trainer: deterministic numpy Adam (seed 42), so the artifact is reproducible.
+- Dataset: UCI Heart Disease (Cleveland), 303 samples, 13 features, 2 classes.
+
+Part of [xaitalk](https://huggingface.co/xaitalk) — cross-framework XAI.