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GeistBERT/
GeistBERT_base

Name: GeistBERT_base
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TUM/GottBERT_filtered_base_best

finetune

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GeistBERT

GeistBERT is a German language model trained on a for the most part deduplicated corpus including OSCAR23, OPUS, and MC4. It builds on GottBERT while introducing Whole Word Masking (WWM) to improve contextual language representation. Achieving state-of-the-art among base models, the model also performs competitively with larger ones on several German NLP benchmarks.

Training Data

GeistBERT was trained on a diverse German corpus combining:

OSCAR23, OPUS, and MC4 (for the most part deduplicated)
German Wikipedia
OpenLegalData
Europarl, EUbookshop, ECB, and EuroPat
OpenSubtitles and TildeMODEL

The dataset amounts to approximately 1.3T tokens, shuffled for improved variance.

Training Procedure

Hardware

Training was conducted on multiple GPUs, including NVIDIA RTX 3090 (24GB VRAM).

Hyperparameters

Parameter	Value
Model Architecture	RoBERTa (Base)
Batch Size	8,000
Training Steps	100k
Weight Initialization	GottBERT filtered base
Warmup Iterations	10k
Peak Learning Rate	0.0007
Learning Rate Decay	Polynomial to zero

Performance

GeistBERT achieves SOTA results on multiple tasks:

NER: CoNLL 2003, GermEval 2014
Text Classification: GermEval 2018 (coarse & fine), 10kGNAD
NLI: German subset of XNLI

Mertics:

NER and Text Classification: F1 Score
NLI: Accuracy

Details:

bold values indicate the best performing model within one architecure (base, large), undescored values the second best.

Model	Accuracy NLI	GermEval_14 F1	CoNLL F1	Coarse F1	Fine F1	10kGNAD F1
GeistBERT	82.67	88.47	86.17	79.67	66.42	90.89
GottBERT_base_best	80.82	87.55	85.93	78.17	53.30	89.64
GottBERT_base_last	81.04	87.48	85.61	78.18	53.92	90.27
GottBERT_filtered_base_best	80.56	87.57	86.14	78.65	52.82	89.79
GottBERT_filtered_base_last	80.74	87.59	85.66	78.08	52.39	89.92
GELECTRA_base	81.70	86.91	85.37	77.26	50.07	89.02
GBERT_base	80.06	87.24	85.16	77.37	51.51	90.30
dbmdzBERT	68.12	86.82	85.15	77.46	52.07	90.34
GermanBERT	78.16	86.53	83.87	74.81	47.78	90.18
XLM-R_base	79.76	86.14	84.46	77.13	50.54	89.81
mBERT	77.03	86.67	83.18	73.54	48.32	88.90
GottBERT_large	82.46	88.20	86.78	79.40	54.61	90.24
GottBERT_filtered_large_best	83.31	88.13	86.30	79.32	54.70	90.31
GottBERT_filtered_large_last	82.79	88.27	86.28	78.96	54.72	90.17
GELECTRA_large	86.33	88.72	86.78	81.28	56.17	90.97
GBERT_large	84.21	88.72	87.19	80.84	57.37	90.74
XLM-R_large	84.07	88.83	86.54	79.05	55.06	90.17

Intended Use

This model is designed for German NLP tasks, including:

Text classification
Named Entity Recognition (NER)
Machine Translation Pre-training
Document Understanding

Limitations

Trained on unfiltered data, meaning some redundant or lower-quality samples may be present.
While deduplication was applied to specific subcorpora, the full corpus was not manually curated.

Fairseq Checkpoint

Get the fairseq checkpoint here.

Citations

If you use GeistBERT in your research, please cite the following paper:

@book{scheible-schmitt-frei-2025-geistbert,
  author    = {\textbf{Scheible-Schmitt}, \textbf{Raphael}  and  Frei, Johann},
  title     = {GeistBERT: Breathing Life into German NLP},
  booktitle      = {Proceedings of the Workshop on Beyond English: Natural Language Processing for all Languages in an Era of Large Language Models},
  month          = {September},
  year           = {2025},
  address        = {Varna, Bulgaria},
  publisher      = {INCOMA Ltd., Shoumen, BULGARIA},
  pages     = {42--50},
  abstract  = {Advances in transformer-based language models have highlighted the benefits of language-specific pre-training on high-quality corpora. In this context, German NLP stands to gain from updated architectures and modern datasets tailored to the linguistic characteristics of the German language. GeistBERT seeks to improve German language processing by incrementally training on a diverse corpus and optimizing model performance across various NLP tasks. We pre-trained GeistBERT using fairseq, following the RoBERTa base configuration with Whole Word Masking (WWM), and initialized from GottBERT weights. The model was trained on a 1.3 TB German corpus with dynamic masking and a fixed sequence length of 512 tokens. For evaluation, we fine-tuned the model on standard downstream tasks, including NER (CoNLL 2003, GermEval 2014), text classification (GermEval 2018 coarse/fine, 10kGNAD), and NLI (German XNLI), using $F_1$ score and accuracy as evaluation metrics. GeistBERT achieved strong results across all tasks, leading among base models and setting a new state-of-the-art (SOTA) in GermEval 2018 fine text classification. It also outperformed several larger models, particularly in classification benchmarks. To support research in German NLP, we release GeistBERT under the MIT license.},
  url       = {https://aclanthology.org/2025.globalnlp-1.6},
  doi       = {https://doi.org/10.26615/978-954-452-105-9-006}
}

GeistBERT/GeistBERT_base