yeniguno/opus-mt-en-tr-kafkaesque

Model Card for yeniguno/marianmt-en-tr-kafkaesque

A fine-tuned MarianMT model that translates English prose into Turkish with a deliberate “Kafkaesque” flavour.
The checkpoint starts from the bilingual Helsinki-NLP/opus-mt-en-tr base model and is further trained on ~10 k parallel sentences taken from published Turkish & English versions of Franz Kafka’s works.
The goal was purely experimental:

Can a compact MT model be nudged toward a specific literary voice by exposing it to a small, style-consistent corpus?

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Model Details

Base architecture	MarianMT (Transformer encoder-decoder)
Source languages	en (contemporary English)
Target language	tr (modern Turkish)
Training corpus	10 014 sentence pairs manually aligned from Turkish editions of Kafka’s short stories and their authorised English translations
Framework	🤗 Transformers ≥ 4.40
License	Apache-2.0 for the model code + weights ✧ ⚠️ Translations used for fine-tuning may still be under copyright; see “Data & Copyright” below

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Intended Uses & Scope

You can	You should not
Generate draft Turkish renderings of Kafka excerpts originally translated into English	Assume output is authoritative or publication-ready
Explore style-transfer / literary MT research	Rely on the model for technical, legal or medical translation
Use as a starting point for further stylistic fine-tuning	Expect high accuracy outside Kafka’s narrative domain

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Training Procedure

• Hardware: 1× A100 40 GB (Google Colab Pro)
• Hyper-params: 5 epochs, batch 16 (eff.), LR 5 × 10⁻⁵, linear decay, warm-up 200 steps
• Early stopping: patience 3 (@ 500-step evals) monitored on BLEU
• Best checkpoint: step 2 500
  • Train loss ≈ 0.42 → Val loss ≈ 1.01
  • SacreBLEU (500-sent dev) baseline 24.4 → tuned 31.8

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Quick Start

from transformers import MarianMTModel, MarianTokenizer

tr_en_model_name = "yeniguno/opus-mt-en-tr-kafkaesque"
tokenizer = MarianTokenizer.from_pretrained(tr_en_model_name)
model = MarianMTModel.from_pretrained(tr_en_model_name)

turkish_text ="My neighbor, at the same peculiar hour each night, left his room with a small, locked bag in hand."

inputs = tokenizer(turkish_text, return_tensors="pt", padding=True)
output_ids = model.generate(**inputs)
print(tokenizer.decode(output_ids[0], skip_special_tokens=True))