German's Next Language Model
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Abstract (arXiv)
In this work we present the experiments which lead to the creation of our BERT and ELECTRA based German language models, GBERT and GELECTRA. By varying the input training data, model size, and the presence of Whole Word Masking (WWM) we were able to attain SoTA performance across a set of document classification and named entity recognition (NER) tasks for both models of base and large size. We adopt an evaluation driven approach in training these models and our results indicate that both adding more data and utilizing WWM improve model performance. By benchmarking against existing German models, we show that these models are the best German models to date. Our trained models will be made publicly available to the research community.
Key facts. Cited by 144 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Oct 2020.
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Citing papers per year: 2021: 21; 2022: 31; 2023: 27; 2024: 32; 2025: 19; 2026: 14.
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50 newest of 144 All 144 (JSON)
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- 2021Quality at a Glance: An Audit of Web-Crawled Multilingual Datasetscited by 241
- 2021MultiEURLEX -- A multi-lingual and multi-label legal document classification dataset for zero-shot cross-lingual transfercited by 89
- 2021WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language modelscited by 89
- 2023LEXTREME: A Multi-Lingual and Multi-Task Benchmark for the Legal Domaincited by 68
- 2022Gender Bias in Masked Language Models for Multiple Languagescited by 60
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- 2022Speaking Multiple Languages Affects the Moral Bias of Language Modelscited by 34
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- 2026Assessing Quality of Experience in Natural Language Generation of German Text19 Aug 2026
- 2026Audio Sentiment Analysis via Distillation and Cross-Modal Integration of Generated Multilingual Transcripts7 Jul 2026
- 2026Comparing BERT Sentence-Pair Classification and Few-Shot LLM Prompting for Detecting Threat and Solution Framing in German Climate News25 Jun 2026
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