Marian Fine-tuned English-French Translation Model
Model Description
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr, specifically trained for English to French translation. The base model was further trained on the KDE4 dataset to improve translation quality for technical and software-related content.
Model Training Details
Training Dataset
- Dataset: KDE4 Dataset (English-French parallel corpus)
- Split Distribution:
- Training set: 189,155 examples (90%)
- Test set: 21,018 examples (10%)
Training Configuration
- Base Model: Helsinki-NLP/opus-mt-en-fr
- Training Arguments:
- Learning rate: 2e-5
- Batch size: 32 (training), 64 (evaluation)
- Number of epochs: 10
- Weight decay: 0.01
- FP16 training enabled
- Evaluation strategy: Before and after training
- Checkpoint saving: Every epoch (maximum 3 saved)
- Training device: GPU with mixed precision (fp16)
Model Results
Evaluation Metrics
The model was evaluated using the BLEU score. The evaluation results before and after training are summarized in the table below:
| Stage | Eval Loss | BLEU Score |
|---|---|---|
| Before Training | 1.700 | 38.97 |
| After Training | 0.796 | 54.96 |
Training Loss
The training loss decreased over the epochs, indicating that the model was learning effectively. The final training loss was approximately 0.710.
Model Usage
from transformers import pipeline
model_checkpoint = "Prikshit7766/marian-finetuned-kde4-en-to-fr"
translator = pipeline("translation", model=model_checkpoint)
translator("Default to expanded threads")
Example Output
[{'translation_text': 'Par défaut, développer les fils de discussion'}]
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Base model
Helsinki-NLP/opus-mt-en-fr