Add link to trained baseline classifier

README.md

@@ -25,6 +25,11 @@ Dataset** product. It contains roughly **1 / 60th of the full dataset** at
 identical schema, label distribution, and statistical fingerprint, so you can
 evaluate fit before licensing the full product.
 
+> 🤖 **Trained baseline available:**
+> [**xpertsystems/cyb001-baseline-classifier**](https://huggingface.co/xpertsystems/cyb001-baseline-classifier)
+> — XGBoost + PyTorch MLP, copy-paste inference notebook, full metrics and
+> honest limitations in the model card.
+
 | File                    | Rows (sample) | Rows (full)   | Description                                |
 |-------------------------|---------------|---------------|--------------------------------------------|
 | `network_topology.csv`  | ~200   | ~3,200        | Network segments and defender configs      |
@@ -48,6 +53,24 @@ management plane, zero-trust, guest Wi-Fi, data centre spine), with:
 
 All IP addresses are SHA-256 pseudonyms (`IP_<12 hex>`) — no real network data.
 
+## Trained Baseline Available
+
+A working baseline classifier trained on this sample is published at
+**[xpertsystems/cyb001-baseline-classifier](https://huggingface.co/xpertsystems/cyb001-baseline-classifier)**.
+
+| Component | Detail |
+|---|---|
+| Task | 3-class flow classification (`BENIGN` / `MALICIOUS` / `AMBIGUOUS`) |
+| Models | XGBoost (`model_xgb.json`) + PyTorch MLP (`model_mlp.safetensors`) |
+| Features | 101 (after one-hot encoding); pipeline included as `feature_engineering.py` |
+| Demo | `inference_example.ipynb` — end-to-end copy-paste |
+| Headline metrics | XGBoost test accuracy 0.998, macro-F1 0.996 — synthetic; see model card for limitations |
+
+This is a reference baseline, not a production IDS. The model card documents
+the calibrated signals it picks up, an ablation showing the model is not
+session-dominated, and six explicit limitations including the gap between
+synthetic and real-world traffic.
+
 ## Calibrated Benchmark Targets
 
 The full product is calibrated to 12 benchmark metrics; the sample preserves
@@ -133,6 +156,10 @@ flows_enriched = flows.merge(topology, on="segment_id", how="left")
 y = (flows["label"] == "MALICIOUS").astype(int)
 ```
 
+For a worked end-to-end example including the 3-class classification target,
+feature engineering, and predictions, see the inference notebook in the
+[baseline classifier repo](https://huggingface.co/xpertsystems/cyb001-baseline-classifier/blob/main/inference_example.ipynb).
+
 ## License
 
 This **sample** is released under **CC-BY-NC-4.0** (free for non-commercial