README.md

---
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


```python
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

```plaintext
[{'translation_text': 'Par défaut, développer les fils de discussion'}]
```