This tutorial section shows previews of SageMaker JumpStart example notebooks that demonstrate how to use the SageMaker JumpStart Industry Python SDK, how to run processing jobs for loading finance documents, parsing texts, computing scores based on NLP score types and corresponding word lists, and creating a multimodal (TabText) dataset. Using the processed and enhanced multimodal dataset, you’ll learn how to fine-tune pretrained BERT language models and deploy them to make predictions.


The SageMaker JumpStart Industry example notebooks are hosted and runnable only through SageMaker Studio. Log in to the SageMaker console, and launch SageMaker Studio. For instructions on how to access the notebooks, see SageMaker JumpStart and SageMaker JumpStart Industry in the Amazon SageMaker Developer Guide.


The example notebooks are for demonstrative purposes only. The notebooks are not financial advice and should not be relied on as financial or investment advice.