Update README.md

README.md

@@ -68,21 +68,3 @@ Implemented in PyTorch via the PyOD library:
 * **Hyperparameter Tuning:** Optimized via Bayesian optimization (Optuna, TPE sampler) on a 10% validation split (~1,789 structures).
 * **Objective:** Minimize Mean Squared Error (MSE) reconstruction loss on validation set.
 
-## How to Get Started
-To use this model, ensure you have `pyod` and `torch` installed.
-
-```python
-import job_lib
-import torch
-from pyod.models.auto_encoder_torch import AutoEncoder
-
-# 1. Load the scaler and standardize your RCSB embeddings
-scaler = joblib.load('scaler.pkl')
-scaled_embeddings = scaler.transform(raw_embeddings)
-
-# 2. Load the model (ensure PyOD environment is set up)
-# model = joblib.load('model.pkl') 
-
-# 3. Predict
-anomaly_scores = model.decision_function(scaled_embeddings)
-labels = model.predict(scaled_embeddings) # 0 for inliers (PI-like), 1 for outliers