metadata

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: 9.95
            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
Env : 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.