Publications

The effectiveness of embedding methods is crucial for optimizing text classification performance in Automated Machine Learning (AutoML). However, selecting the most suitable pre-trained model for a given task remains challenging. This study introduces the Corpus-Driven Domain Mapping (CDDM) pipeline, which utilizes a domain-annotated corpus of pre-fine-tuned models from the Hugging Face Model Hub to improve model selection. Integrating these models into AutoML systems significantly boosts classification performance across multiple datasets compared to baseline methods. Despite some domain recognition inaccuracies, results demonstrate CDDM’s potential to enhance model selection, streamline AutoML workflows, and reduce computational costs.

The Trend Survey Research Data Infrastructures 2024 is part of the accompanying research of the Basic Services for the National Research Data Infrastructure (Base4NFDI). The trend survey captures the perception, use and evaluation of established and new data infrastructures and services in the German research landscape. The focus in on the perspective of (potential) users.

I explain that -capture program drop- is useless in ado-files. While it prevents errors in do-files when redefining programs in memory, it either isn't executed or results in an error in ado-files. Moreover, in ado-files with local subroutines, -capture program drop- can mistakenly remove unrelated programs from memory.