aiml-sentiment-calibration

What problem does it solve?

This skill provides a structured framework to evaluate and calibrate multilingual sentiment classifiers against real-world review data, ensuring consistent cross-language performance and alignment with target ratings.

Core Features & Use Cases

• Evaluation framework for multilingual sentiment models using a labeled reviews dataset.
• Cross-language calibration across languages supported by the underlying model (e.g., English, Spanish, Chinese, etc.).
• Use Case: Validate that model predictions align with target star ratings (1-5) within a tolerance, enabling robust model QA for safety-sensitive applications.

Quick Start

Run the calibrate.py script to evaluate your multilingual sentiment model on the provided reviews.json dataset.