aiml-sentiment-calibration

Calibrating a multilingual sentiment classifier involves loading reviews.json, mapping sentiment labels to star ratings, and running Python scripts to measure accuracy within a defined tolerance. This benchmarks cross-language performance for safety-sensitive applications.

Benchmarking sentiment model accuracy across languages requires evaluating predictions against a labeled reviews dataset using a defined tolerance threshold. This cross-language calibration workflow ensures consistent domain performance and robust quality assurance.

Yes, this sentiment calibration workflow requires the transformers and torch dependencies. These frameworks provide the underlying model inference and tensor operations needed to evaluate multilingual sentiment predictions against a labeled review dataset.

To test sentiment model calibration, run the test_calibration.py script on the provided reviews.json dataset. This evaluates whether model predictions align with target star ratings within your defined tolerance threshold.

Yes, you can use this calibration framework for safety-related AI evaluation tasks. It enables robust model QA by validating that multilingual sentiment predictions consistently align with target star ratings within a defined tolerance.