There are a few ways to determine when to stop training a natural language understanding (NLU) model:

Monitoring the performance on a validation set: One approach is to monitor the performance of the model on a validation set during training and stop training when the performance on the validation set stops improving or starts to degrade. This can help to prevent overfitting and ensure that the model generalizes well to new data.

Using early stopping: Another approach is to use early stopping, which involves setting a maximum number of epochs and stopping training when the performance on the validation set has not improved for a certain number of epochs. This can help to prevent overfitting by stopping training when the model is no longer making progress.

Using human evaluation: If you have access to human annotators, you can also use human evaluation to determine when the model is ready for production. You can use a subset of your data as a test set and have the annotators evaluate the model's performance on this test set. When the model's performance meets your desired accuracy threshold, you can consider it ready for production.

Ultimately, the best way to determine when a model is production-ready will depend on the specific requirements of your application and the resources available to you. It may be helpful to experiment with different approaches and see which one works best for your particular case.