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---
library_name: transformers
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-en-fr
tags:
- translation
- generated_from_trainer
datasets:
- kde4
metrics:
- bleu
model-index:
- name: marian-finetuned-kde4-en-to-fr
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: kde4
type: kde4
config: en-fr
split: train
args: en-fr
metrics:
- name: Bleu
type: bleu
value: 50.54449537679619
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Marian Fine-Tuned KDE4 (English-to-French)
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) using the KDE4 dataset. It achieves the following results on the evaluation set:
- **Loss**: 0.9620
- **BLEU**: 50.5445
---
## Model Description
This English-to-French translation model has been fine-tuned specifically on the KDE4 dataset. The base model, Helsinki-NLP/opus-mt-en-fr, is part of the MarianMT family, renowned for its efficiency and high-quality neural machine translation capabilities.
---
## Intended Uses & Limitations
### Intended Uses
- Translating English text into French.
- High-quality translations in the context of software localization, especially related to KDE4.
### Limitations
- Performance may decline on texts outside the KDE4 domain.
- Struggles with idiomatic expressions, specialized technical jargon, or ambiguous content.
---
## Training & Evaluation Data
The model was fine-tuned on the KDE4 dataset, a specialized resource for machine translation in software localization. The evaluation metrics reflect the model's performance on this domain-specific data.
---
## Training Procedure
### Hyperparameters
- **Learning Rate**: 2e-05
- **Train Batch Size**: 32
- **Eval Batch Size**: 64
- **Seed**: 42
- **Optimizer**: AdamW with `betas=(0.9, 0.999)`, `epsilon=1e-08`
- **LR Scheduler**: Linear
- **Epochs**: 1
- **Mixed Precision Training**: Native AMP
### Results
- **Loss**: 0.9620
- **BLEU**: 50.5445
### Training Loss Progression
| Step | Training Loss |
|-------|---------------|
| 500 | 1.2253 |
| 1000 | 1.2165 |
| 1500 | 1.1913 |
| 2000 | 1.1404 |
| 2500 | 1.1178 |
| 3000 | 1.0900 |
| 3500 | 1.0594 |
| 4000 | 1.0512 |
| 4500 | 1.0633 |
| 5000 | 1.0405 |
| 5500 | 1.0316 |
---
## Framework Versions
- **Transformers**: 4.47.1
- **PyTorch**: 2.5.1+cu121
- **Datasets**: 3.2.0
- **Tokenizers**: 0.21.0
---
## Example Usage
```python
from transformers import pipeline
# Load the model
model_checkpoint = "ParitKansal/marian-finetuned-kde4-en-to-fr"
translator = pipeline("translation", model=model_checkpoint)
# Translate text
translation = translator("Default to expanded threads")
print(translation)
```
This script demonstrates how to use the model for English-to-French translation tasks.
--- |