TaxBERT
This repository accompanies the paper: Hechtner, F., Schmidt, L., Seebeck, A., & Weiß, M. (2025). How to design and employ specialized large language models for accounting and tax research: The example of TaxBERT. TaxBERT is a domain-adapated RoBERTa model, specifically designed to analyze qualitative corporate tax disclosures.
In the future, we will add the following features:
- Tax Sentence Recognition
- Tax Risk Sentiment
SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5146523 The paper provides an ‘A-to-Z’ description of how to design and employ specialized Bidirectional Encoder Representation of Transformers (BERT) models that are environmentally sustainable and practically feasible for accounting and tax researchers.
GitHub: https://github.com/TaxBERT/TaxBERT
If the following Guide/Repository is used for academic or scientific purposes, please cite the paper.
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