This is a monolingual continued-pretraining control checkpoint reported in the paper table. It is provided for reproducibility of the baseline comparison.

Paper name: DE-Base-CP
Model role: baseline-control
Source/base model: GermanT5/t5-efficient-gc4-german-base-nl36
Code and pipeline: GitHub repository
Architecture: T5 encoder-decoder
SciLaD dataset: scilons/SciLaD-all-text-v1
Evaluation benchmark: Global-MMLU

Evaluation

Zero-shot Global-MMLU accuracy reported by the paper aggregation:

Metric	Accuracy
Average	22.95
STEM	21.25
Humanities	24.21
Social Sciences	21.71
Other	23.98

Limitations

The model is evaluated primarily with zero-shot Global-MMLU. Downstream task-specific evaluation is recommended before deployment in specialized scientific workflows.

Citation

Title: Transferring Scientific English Pre-Trained Language Models to Multiple Languages Using Cross-Lingual Transfer
Authors: Nikolas Rauscher, Fabio Barth, Georg Rehm
Venue: LREC-COLING 2026, citation details TBA after publication

rausch/de-t5-base-sci-cp-15k

DE-Base-CP

Model Details

Evaluation

Limitations

Citation

rausch/
de-t5-base-sci-cp-15k