---
language:
- pl
base_model:
- allegro/plt5-base
datasets:
- scilons/SciLaD-all-text-v1
library_name: transformers
tags:
- t5
- seq2seq
- text-to-text
- scientific-language-models
- cross-lingual-transfer
- wechsel
- global-mmlu
---

# PL-Base-CP

Polish monolingual base model continued on the SciLaD target-language split as a 15k-step control baseline.

## Model Details

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

- Paper name: `PL-Base-CP`
- Model role: `baseline-control`
- Source/base model: [allegro/plt5-base](https://huggingface.co/allegro/plt5-base)
- Code and pipeline: [GitHub repository](https://github.com/nikolas-rauscher/scientific-english-crosslingual-transfer)
- Architecture: T5 encoder-decoder
- SciLaD dataset: [scilons/SciLaD-all-text-v1](https://huggingface.co/datasets/scilons/SciLaD-all-text-v1)
- Evaluation benchmark: [Global-MMLU](https://huggingface.co/datasets/CohereLabs/Global-MMLU)

## Evaluation

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

| Metric | Accuracy |
|---|---:|
| Average | 24.65 |
| STEM | 23.88 |
| Humanities | 24.51 |
| Social Sciences | 23.43 |
| Other | 26.87 |

## 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