Create README.md

README.md

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+---
+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.