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README.md

@@ -186,32 +186,18 @@ SynSEPA generates realistic SEPA payment sequences with:
 
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-## File Structure
-
-```
-synsep-dataset/
-├── data/
-│   ├── synsep_full_dataset.csv    ← Main dataset (1.84M transactions, ~298 MB)
-│   └── accounts.csv               ← Account metadata (10,000 accounts, ~3.9 MB)
-├── code/
-│   ├── generate/                  ← Scripts to regenerate the dataset from scratch
-│   └── examples/
-│       └── sample_baseline.py     ← Quick start: Isolation Forest baseline
-├── README.md                      ← This file
-└── dataset-metadata.json          ← Kaggle dataset metadata
-```
-
----
-
 ## Quick Start
 
-```bash
-pip install pandas scikit-learn matplotlib numpy
-python code/examples/sample_baseline.py
+```python
+from datasets import load_dataset
+
+transactions = load_dataset("EpiphanyTech/SynSEPA", "transactions")
+accounts = load_dataset("EpiphanyTech/SynSEPA", "accounts")
 ```
 
-This runs an Isolation Forest baseline on the dataset and prints AUROC, PR-AUC,
-and a classification report at 1% FPR — a useful starting benchmark.
+An Isolation Forest baseline (AUROC, PR-AUC, classification report at 1% FPR)
+and the full data-generation pipeline are available in the
+[GitHub repo](https://github.com/epiphanytech/SynSEPA).
 
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