Simulated annotations from two annotators [chapter] deterministic

annotator_1 = np.array([0, 1, 1, 0, 1, 0, 1, 1, 0, 0]) annotator_2 = np.array([0, 1, 0, 0, 1, 1, 1, 1, 0, 0]) kappa = cohen_kappa_score(annotator_1, annotator_2) print(f"Cohen's Kappa: {kappa:.3f}")

`

A kappa value close to 1 indicates strong agreement, while values near 0 suggest random labeling. If kappa is low, you should investigate labeling guidelines, provide additional training to annotators, or introduce automated pre-screening.

Personalization and Dynamic Personas As your model accumulates annotated data, you can use it to personalize outputs for individual users. **Personalization** involves modifying products or services based on specific requests or preferences, and in AI systems, it often means adapting the model’s behavior to match expectations. By leveraging **PERSONA_KEYS**, you can create dynamic personas that evolve with the ’s knowledge. Below is a simple example that retrieves a persona’s keys and uses them to guide generation. This demonstrates how annotation data can be transformed into personalized behavior.

```python