Hi Will, when deploying a model to a Sagemaker endpoint you can provide a custom inference script in which you can control the behaviour of the model for inference requests. You can find the documentation on how to do this here: Deploy models to Amazon SageMaker
In your particular scenario I would think hat you can override the predict_fn and/or the output_fn methods to get to the desired result. You can find an example for an inference.py file here 2 (note that this particular file is for text summarization, so it won’t apply for your use case).
To see what code you need to get all scores returned you can refer back to the code from the TextClassification Pipeline, that you have already mentioned: transformers/text_classification.py at master · huggingface/transformers · GitHub 2. If you implement this code into your inference script you should be all set.
Hope that helps, let me know if any questions.
Cheers
Heiko