Hugging Face, a provider of natural language modeling APIs, simplifies working with large transformer models, facilitating tasks such as text generation, translation, and classification. The integration with Comet allows for seamless auto-logging of model metrics and parameters without modifying the source code, leveraging Hugging Face's Trainer/TFTrainer object. Users can begin by installing Comet and setting environment variables for the Comet API key and project name. Through a grid search of hyperparameters like batch size, learning rate, and weight decay on BERT models, Comet captures and visualizes training metrics in real-time, including loss, accuracy, and F1 score. Advanced features like confusion matrices can be logged using the compute_metrics function. The integration supports both TensorFlow and PyTorch models, enabling detailed comparisons of different model versions and parameter settings through tools such as Parallel Coordinates charts. Comet provides flexibility in logging with environment variables that control the logging mode, allowing users to choose between online, offline, or disabling logging.