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Generative, pre-trained transformers (GPTs, a.k.a. "Foundation Models") havereshaped natural language processing (NLP) through their versatility in diversedownstream tasks. However, their potential extends far beyond NLP. This paperprovides a software utility to help realize this potential, extending theapplicability of GPTs to continuous-time sequences of complex events withinternal dependencies, such as medical record datasets. Despite theirpotential, the adoption of foundation models in these domains has been hamperedby the lack of suitable tools for model construction and evaluation. To bridgethis gap, we introduce Event Stream GPT (ESGPT), an open-source librarydesigned to streamline the end-to-end process for building GPTs forcontinuous-time event sequences. ESGPT allows users to (1) build flexible,foundation-model scale input datasets by specifying only a minimalconfiguration file, (2) leverage a Hugging Face compatible modeling API forGPTs over this modality that incorporates intra-event causal dependencystructures and autoregressive generation capabilities, and (3) evaluate modelsvia standardized processes that can assess few and even zero-shot performanceof pre-trained models on user-specified fine-tuning tasks.

Event Stream GPT: A Data Pre-processing and Modeling Library for  Generative, Pre-trained Transformers over Continuous-time Sequences of  Complex Events