German FinBERT: A German Pre-trained Language Model
Abstract
This study presents German FinBERT, a novel pre-trained German language model tailored for financial textual data. The model is trained through a comprehensive pre-training process, leveraging a substantial corpus comprising financial reports, ad-hoc announcements and news related to German companies. The corpus size is comparable to the data sets commonly used for training standard BERT models. I evaluate the performance of German FinBERT on downstream tasks, specifically sentiment prediction, topic recognition and question answering against generic German language models. My results demonstrate improved performance on finance-specific data, indicating the efficacy of German FinBERT in capturing domain-specific nuances. The presented findings suggest that German FinBERT holds promise as a valuable tool for financial text analysis, potentially benefiting various applications in the financial domain.
Keywords
Cite
@article{arxiv.2311.08793,
title = {German FinBERT: A German Pre-trained Language Model},
author = {Moritz Scherrmann},
journal= {arXiv preprint arXiv:2311.08793},
year = {2023}
}