metadata

datasets:
  - Helsinki-NLP/kde4
language:
  - en
  - fr
metrics:
  - sacrebleu
base_model:
  - Helsinki-NLP/opus-mt-en-fr
pipeline_tag: translation
library_name: transformers

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'}]