---
license: mit
language:
  - en
  - nl
tags:
  - machine-translation
  - low-resource
  - creativity
library_name: transformers
pipeline_tag: translation
model-index:
  - name: EN-FR → EN-NL • Non-Creative
    results:
      - task:
          type: machine-translation
          name: Translation
        dataset:
          name: Dutch Parallel Corpus Journalistic texts
          type: Helsinki-NLP/open_subtitles
          split: test
        metrics:
          - type: sacrebleu
            name: SacreBLEU
            value: 9.950
            greater_is_better: true
---

# EN-DE parent ➜ EN-NL fine-tuned on creative corpus

**Authors:** Niek Holter  
**Thesis:** “Transferring Creativity”

## Summary
This model starts from Helsinki-NLP’s MarianMT `opus-mt-en-fr` and is fine-tuned on a 10k-sentence **non-creative** English–Dutch corpus (Journalistic texts).  
It is one of four systems trained for my bachelor’s thesis to study how transfer-learning settings affect MT creativity.

| Parent model | Fine-tune data | BLEU | COMET | Transformer Creativity Score |
|-------------|----------------|------|-------|------------------|
| en-de       | Creative       | 9.950 | 0.574 | 0.34 |

## Intended use
* Research on creative MT and low-resource transfer learning

## Training details
* Hardware  : NVIDIA GTX 1070 (CUDA 12.1)  
* Epochs     : Early-stopped ≤ 200 (patience 5)  
* LR / batch : 2 e-5 / 16  
* Script     : [`finetuning.py`](./finetuning.py)  
* Env        : [`environment.yml`](./environment.yml)

## Data
* **Non-Creative corpus** 10k sentences from DPC Journalistic texts.  
* Sentence-level 1:1 alignments; deduplicated to avoid leakage.  
See https://github.com/muniekstache/Transfer-Creativity.git for full pipeline.