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--- |
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license: mit |
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language: |
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- en |
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- nl |
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tags: |
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- machine-translation |
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- low-resource |
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- creativity |
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library_name: transformers |
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pipeline_tag: translation |
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model-index: |
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- name: EN-DE → EN-NL • Non-Creative |
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results: |
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- task: |
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type: machine-translation |
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name: Translation |
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dataset: |
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name: Dutch Parallel Corpus Journalistic texts |
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type: Helsinki-NLP/open_subtitles |
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split: test |
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metrics: |
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- type: sacrebleu |
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name: SacreBLEU |
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value: 12.730 |
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greater_is_better: true |
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--- |
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# EN-DE parent ➜ EN-NL fine-tuned on creative corpus |
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**Authors:** Niek Holter |
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**Thesis:** “Transferring Creativity” |
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## Summary |
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This model starts from Helsinki-NLP’s MarianMT `opus-mt-en-de` and is fine-tuned on a 10k-sentence **non-creative** English–Dutch corpus (Journalistic texts DPC). |
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It is one of four systems trained for my bachelor’s thesis to study how transfer-learning settings affect MT creativity. |
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| Parent model | Fine-tune data | BLEU | COMET | Transformed Creativity Score | |
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|-------------|----------------|------|-------|------------------| |
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| en-de | Creative | 12.7 | 0.626 | 0.38 | |
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## Intended use |
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* Research on creative MT and low-resource transfer learning |
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## Training details |
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* Hardware : NVIDIA GTX 1070 (CUDA 12.1) |
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* Epochs : Early-stopped ≤ 200 (patience 5) |
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* LR / batch : 2 e-5 / 16 |
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* Script : [`finetuning.py`](./finetuning.py) |
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* Env : [`environment.yml`](./environment.yml) |
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## Data |
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* **Non-Creative corpus** 10k sentences from DPC Journalistic texts. |
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* Sentence-level 1:1 alignments; deduplicated to avoid leakage. |
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See https://github.com/muniekstache/Transfer-Creativity.git for full pipeline. |
